CLIA HNSCC UNM Clinical Analysis 02272025
library(swimplot) library(grid) library(gtable) library(readr) library(mosaic) library(dplyr) library(survival) library(survminer) library(ggplot2) library(scales) library(coxphf) library(ggthemes) library(tidyverse) library(gtsummary) library(flextable) library(reshape2) library(parameters) library(car) library(ComplexHeatmap) library(tidyverse) library(readxl) library(janitor) library(DT) library(pROC) library(rms)
#ctDNA Detection Rates by Window and Stages
#ctDNA at Baseline
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_data$ctDNA.Base <- factor(circ_data$ctDNA.Base, levels=c("NEGATIVE","POSITIVE"))
circ_data <- subset(circ_data, ctDNA.Base %in% c("NEGATIVE", "POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("I/II","III/IVA/IVB","IVC"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.Base == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.Base, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
Stage = total_counts_by_stage$Group.1,
Total_Count = total_counts_by_stage$x,
Positive_Count = positive_counts_by_stage$x,
Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100 # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.Base == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100 # Convert to percentage
overall_row <- data.frame(
Stage = "Overall",
Total_Count = overall_total_count,
Positive_Count = overall_positive_count,
Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)
#ctDNA at MRD
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("I/II","III/IVA/IVB","IVC"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.MRD == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.MRD, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
Stage = total_counts_by_stage$Group.1,
Total_Count = total_counts_by_stage$x,
Positive_Count = positive_counts_by_stage$x,
Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100 # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.MRD == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100 # Convert to percentage
overall_row <- data.frame(
Stage = "Overall",
Total_Count = overall_total_count,
Positive_Count = overall_positive_count,
Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)
#ctDNA at Surveillance
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("I/II","III/IVA/IVB","IVC"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.Surveillance == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.Surveillance, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
Stage = total_counts_by_stage$Group.1,
Total_Count = total_counts_by_stage$x,
Positive_Count = positive_counts_by_stage$x,
Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100 # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.Surveillance == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100 # Convert to percentage
overall_row <- data.frame(
Stage = "Overall",
Total_Count = overall_total_count,
Positive_Count = overall_positive_count,
Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)#Demographics Table
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data_subset <- circ_data %>%
select(
Sex,
Age,
Tobacco.History,
Prim.Location,
cT,
cN,
cM,
Histology,
Stage,
p16.status,
Treatment.Group,
PFS.Event,
OS.Event,
OS.months) %>%
mutate(
Sex = factor(Sex),
Age = as.numeric(Age),
Tobacco.History = factor(Tobacco.History),
Prim.Location = factor(Prim.Location),
cT = factor(cT),
cN = factor(cN),
cM = factor(cM),
Histology = factor(Histology),
Stage = factor(Stage),
p16.status = factor(p16.status),
Treatment.Group = factor(Treatment.Group),
PFS.Event = factor(PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression")),
OS.Event = factor(OS.Event, levels = c("FALSE", "TRUE"), labels = c("Alive", "Deceased")),
OS.months = as.numeric(OS.months))
table1 <- circ_data_subset %>%
tbl_summary(
statistic = list(
all_continuous() ~ "{median} ({min} - {max})",
all_categorical() ~ "{n} ({p}%)")) %>%
bold_labels()
table1| Characteristic | N = 971 |
|---|---|
| Sex | |
| Female | 17 (18%) |
| Male | 80 (82%) |
| Age | 66 (29 - 95) |
| Tobacco.History | 63 (65%) |
| Prim.Location | |
| Larynx/Hypopharynx | 5 (5.2%) |
| Oral cavity | 16 (16%) |
| Oropharynx | 67 (69%) |
| Other (paranasal sinus and nasopharyngeal) | 9 (9.3%) |
| cT | |
| T0 | 2 (2.1%) |
| T1 | 12 (12%) |
| T2 | 31 (32%) |
| T3 | 30 (31%) |
| T4 | 21 (22%) |
| TX | 1 (1.0%) |
| cN | |
| N0 | 22 (23%) |
| N1 | 33 (34%) |
| N2 | 33 (34%) |
| N3 | 9 (9.3%) |
| cM | |
| M0 | 93 (96%) |
| M1 | 4 (4.1%) |
| Histology | |
| Adenosquamous carcinoma | 1 (1.0%) |
| Basaloid squamous cell carcinoma | 6 (6.2%) |
| Epithelial myoepithelial carcinoma | 1 (1.0%) |
| Squamous cell carcinoma | 86 (89%) |
| Undifferentiated carcinoma | 3 (3.1%) |
| Stage | |
| I/II | 49 (51%) |
| III/IVA/IVB | 45 (46%) |
| IVC | 3 (3.1%) |
| p16.status | |
| Negative | 43 (44%) |
| Positive | 54 (56%) |
| Treatment.Group | |
| Definitive CRT or RT | 69 (71%) |
| None (Declined Treatment) | 1 (1.0%) |
| None (Hospice) | 2 (2.1%) |
| Surgery + CRT or RT | 24 (25%) |
| Surgery only | 1 (1.0%) |
| PFS.Event | |
| No Progression | 65 (67%) |
| Progression | 32 (33%) |
| OS.Event | |
| Alive | 81 (84%) |
| Deceased | 16 (16%) |
| OS.months | 22 (2 - 56) |
| 1 n (%); Median (Min - Max) | |
fit1 <- as_flex_table(
table1,
include = everything(),
return_calls = FALSE
)
fit1Characteristic | N = 971 |
|---|---|
Sex | |
Female | 17 (18%) |
Male | 80 (82%) |
Age | 66 (29 - 95) |
Tobacco.History | 63 (65%) |
Prim.Location | |
Larynx/Hypopharynx | 5 (5.2%) |
Oral cavity | 16 (16%) |
Oropharynx | 67 (69%) |
Other (paranasal sinus and nasopharyngeal) | 9 (9.3%) |
cT | |
T0 | 2 (2.1%) |
T1 | 12 (12%) |
T2 | 31 (32%) |
T3 | 30 (31%) |
T4 | 21 (22%) |
TX | 1 (1.0%) |
cN | |
N0 | 22 (23%) |
N1 | 33 (34%) |
N2 | 33 (34%) |
N3 | 9 (9.3%) |
cM | |
M0 | 93 (96%) |
M1 | 4 (4.1%) |
Histology | |
Adenosquamous carcinoma | 1 (1.0%) |
Basaloid squamous cell carcinoma | 6 (6.2%) |
Epithelial myoepithelial carcinoma | 1 (1.0%) |
Squamous cell carcinoma | 86 (89%) |
Undifferentiated carcinoma | 3 (3.1%) |
Stage | |
I/II | 49 (51%) |
III/IVA/IVB | 45 (46%) |
IVC | 3 (3.1%) |
p16.status | |
Negative | 43 (44%) |
Positive | 54 (56%) |
Treatment.Group | |
Definitive CRT or RT | 69 (71%) |
None (Declined Treatment) | 1 (1.0%) |
None (Hospice) | 2 (2.1%) |
Surgery + CRT or RT | 24 (25%) |
Surgery only | 1 (1.0%) |
PFS.Event | |
No Progression | 65 (67%) |
Progression | 32 (33%) |
OS.Event | |
Alive | 81 (84%) |
Deceased | 16 (16%) |
OS.months | 22 (2 - 56) |
1n (%); Median (Min - Max) | |
save_as_docx(fit1, path= "~/Downloads/1. CLIA HNSCC UNM Demographics Table.docx")#Demographics Table by ctDNA at baseline
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data_subset1 <- circ_data %>%
select(
Sex,
Age,
Tobacco.History,
Prim.Location,
cT,
cN,
cM,
Histology,
Stage,
p16.status,
Treatment.Group,
PFS.Event,
OS.Event,
OS.months) %>%
mutate(
Sex = factor(Sex),
Age = as.numeric(Age),
Tobacco.History = factor(Tobacco.History),
Prim.Location = factor(Prim.Location),
cT = factor(cT),
cN = factor(cN),
cM = factor(cM),
Histology = factor(Histology),
Stage = factor(Stage),
p16.status = factor(p16.status),
Treatment.Group = factor(Treatment.Group),
PFS.Event = factor(PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression")),
OS.Event = factor(OS.Event, levels = c("FALSE", "TRUE"), labels = c("Alive", "Deceased")),
OS.months = as.numeric(OS.months))
circ_data1 <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data_subset2 <- circ_data1 %>%
select(
Sex,
Age,
Tobacco.History,
Prim.Location,
cT,
cN,
cM,
Histology,
Stage,
p16.status,
Treatment.Group,
PFS.Event,
OS.Event,
OS.months,
ctDNA.Base) %>%
mutate(
Sex = factor(Sex),
Age = as.numeric(Age),
Tobacco.History = factor(Tobacco.History),
Prim.Location = factor(Prim.Location),
cT = factor(cT),
cN = factor(cN),
cM = factor(cM),
Histology = factor(Histology),
Stage = factor(Stage),
p16.status = factor(p16.status),
Treatment.Group = factor(Treatment.Group),
PFS.Event = factor(PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression")),
OS.Event = factor(OS.Event, levels = c("FALSE", "TRUE"), labels = c("Alive", "Deceased")),
OS.months = as.numeric(OS.months),
ctDNA.Base = factor(ctDNA.Base, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive")))
Overall <- circ_data_subset1 %>%
tbl_summary(
statistic = list(
all_continuous() ~ "{median} ({min} - {max})",
all_categorical() ~ "{n} ({p}%)")) %>%
bold_labels()
Overall| Characteristic | N = 971 |
|---|---|
| Sex | |
| Female | 17 (18%) |
| Male | 80 (82%) |
| Age | 66 (29 - 95) |
| Tobacco.History | 63 (65%) |
| Prim.Location | |
| Larynx/Hypopharynx | 5 (5.2%) |
| Oral cavity | 16 (16%) |
| Oropharynx | 67 (69%) |
| Other (paranasal sinus and nasopharyngeal) | 9 (9.3%) |
| cT | |
| T0 | 2 (2.1%) |
| T1 | 12 (12%) |
| T2 | 31 (32%) |
| T3 | 30 (31%) |
| T4 | 21 (22%) |
| TX | 1 (1.0%) |
| cN | |
| N0 | 22 (23%) |
| N1 | 33 (34%) |
| N2 | 33 (34%) |
| N3 | 9 (9.3%) |
| cM | |
| M0 | 93 (96%) |
| M1 | 4 (4.1%) |
| Histology | |
| Adenosquamous carcinoma | 1 (1.0%) |
| Basaloid squamous cell carcinoma | 6 (6.2%) |
| Epithelial myoepithelial carcinoma | 1 (1.0%) |
| Squamous cell carcinoma | 86 (89%) |
| Undifferentiated carcinoma | 3 (3.1%) |
| Stage | |
| I/II | 49 (51%) |
| III/IVA/IVB | 45 (46%) |
| IVC | 3 (3.1%) |
| p16.status | |
| Negative | 43 (44%) |
| Positive | 54 (56%) |
| Treatment.Group | |
| Definitive CRT or RT | 69 (71%) |
| None (Declined Treatment) | 1 (1.0%) |
| None (Hospice) | 2 (2.1%) |
| Surgery + CRT or RT | 24 (25%) |
| Surgery only | 1 (1.0%) |
| PFS.Event | |
| No Progression | 65 (67%) |
| Progression | 32 (33%) |
| OS.Event | |
| Alive | 81 (84%) |
| Deceased | 16 (16%) |
| OS.months | 22 (2 - 56) |
| 1 n (%); Median (Min - Max) | |
ByctDNA_MRD <- circ_data_subset2 %>%
tbl_summary(
by = ctDNA.Base, # add this line to subgroup by ctDNA.Base
statistic = list(
all_continuous() ~ "{median} ({min} - {max})",
all_categorical() ~ "{n} ({p}%)")) %>%
add_p() %>%
bold_labels()36 missing rows in the "ctDNA.Base" column have been removed.ByctDNA_MRD| Characteristic | Negative N = 71 |
Positive N = 551 |
p-value2 |
|---|---|---|---|
| Sex | 0.10 | ||
| Female | 3 (43%) | 8 (15%) | |
| Male | 4 (57%) | 47 (85%) | |
| Age | 80 (53 - 95) | 65 (37 - 95) | 0.081 |
| Tobacco.History | 4 (57%) | 37 (67%) | 0.7 |
| Prim.Location | 0.037 | ||
| Larynx/Hypopharynx | 0 (0%) | 3 (5.5%) | |
| Oral cavity | 3 (43%) | 4 (7.3%) | |
| Oropharynx | 3 (43%) | 44 (80%) | |
| Other (paranasal sinus and nasopharyngeal) | 1 (14%) | 4 (7.3%) | |
| cT | 0.050 | ||
| T0 | 0 (0%) | 2 (3.6%) | |
| T1 | 1 (14%) | 4 (7.3%) | |
| T2 | 2 (29%) | 19 (35%) | |
| T3 | 0 (0%) | 21 (38%) | |
| T4 | 4 (57%) | 9 (16%) | |
| TX | 0 (0%) | 0 (0%) | |
| cN | >0.9 | ||
| N0 | 1 (14%) | 11 (20%) | |
| N1 | 3 (43%) | 19 (35%) | |
| N2 | 3 (43%) | 19 (35%) | |
| N3 | 0 (0%) | 6 (11%) | |
| cM | >0.9 | ||
| M0 | 7 (100%) | 53 (96%) | |
| M1 | 0 (0%) | 2 (3.6%) | |
| Histology | 0.14 | ||
| Adenosquamous carcinoma | 0 (0%) | 0 (0%) | |
| Basaloid squamous cell carcinoma | 0 (0%) | 3 (5.5%) | |
| Epithelial myoepithelial carcinoma | 0 (0%) | 0 (0%) | |
| Squamous cell carcinoma | 6 (86%) | 52 (95%) | |
| Undifferentiated carcinoma | 1 (14%) | 0 (0%) | |
| Stage | 0.4 | ||
| I/II | 2 (29%) | 32 (58%) | |
| III/IVA/IVB | 5 (71%) | 21 (38%) | |
| IVC | 0 (0%) | 2 (3.6%) | |
| p16.status | 0.090 | ||
| Negative | 5 (71%) | 18 (33%) | |
| Positive | 2 (29%) | 37 (67%) | |
| Treatment.Group | 0.2 | ||
| Definitive CRT or RT | 5 (71%) | 51 (93%) | |
| None (Declined Treatment) | 0 (0%) | 0 (0%) | |
| None (Hospice) | 0 (0%) | 0 (0%) | |
| Surgery + CRT or RT | 2 (29%) | 3 (5.5%) | |
| Surgery only | 0 (0%) | 1 (1.8%) | |
| PFS.Event | 0.4 | ||
| No Progression | 6 (86%) | 35 (64%) | |
| Progression | 1 (14%) | 20 (36%) | |
| OS.Event | 0.3 | ||
| Alive | 7 (100%) | 44 (80%) | |
| Deceased | 0 (0%) | 11 (20%) | |
| OS.months | 31 (21 - 39) | 16 (2 - 45) | 0.028 |
| 1 n (%); Median (Min - Max) | |||
| 2 Fisher’s exact test; Wilcoxon rank sum test | |||
merged_table <- tbl_merge(tbls=list(Overall, ByctDNA_MRD))
merged_table| Characteristic |
Table 1
|
Table 2
|
||
|---|---|---|---|---|
| N = 971 | Negative N = 71 |
Positive N = 551 |
p-value2 | |
| Sex | 0.10 | |||
| Female | 17 (18%) | 3 (43%) | 8 (15%) | |
| Male | 80 (82%) | 4 (57%) | 47 (85%) | |
| Age | 66 (29 - 95) | 80 (53 - 95) | 65 (37 - 95) | 0.081 |
| Tobacco.History | 63 (65%) | 4 (57%) | 37 (67%) | 0.7 |
| Prim.Location | 0.037 | |||
| Larynx/Hypopharynx | 5 (5.2%) | 0 (0%) | 3 (5.5%) | |
| Oral cavity | 16 (16%) | 3 (43%) | 4 (7.3%) | |
| Oropharynx | 67 (69%) | 3 (43%) | 44 (80%) | |
| Other (paranasal sinus and nasopharyngeal) | 9 (9.3%) | 1 (14%) | 4 (7.3%) | |
| cT | 0.050 | |||
| T0 | 2 (2.1%) | 0 (0%) | 2 (3.6%) | |
| T1 | 12 (12%) | 1 (14%) | 4 (7.3%) | |
| T2 | 31 (32%) | 2 (29%) | 19 (35%) | |
| T3 | 30 (31%) | 0 (0%) | 21 (38%) | |
| T4 | 21 (22%) | 4 (57%) | 9 (16%) | |
| TX | 1 (1.0%) | 0 (0%) | 0 (0%) | |
| cN | >0.9 | |||
| N0 | 22 (23%) | 1 (14%) | 11 (20%) | |
| N1 | 33 (34%) | 3 (43%) | 19 (35%) | |
| N2 | 33 (34%) | 3 (43%) | 19 (35%) | |
| N3 | 9 (9.3%) | 0 (0%) | 6 (11%) | |
| cM | >0.9 | |||
| M0 | 93 (96%) | 7 (100%) | 53 (96%) | |
| M1 | 4 (4.1%) | 0 (0%) | 2 (3.6%) | |
| Histology | 0.14 | |||
| Adenosquamous carcinoma | 1 (1.0%) | 0 (0%) | 0 (0%) | |
| Basaloid squamous cell carcinoma | 6 (6.2%) | 0 (0%) | 3 (5.5%) | |
| Epithelial myoepithelial carcinoma | 1 (1.0%) | 0 (0%) | 0 (0%) | |
| Squamous cell carcinoma | 86 (89%) | 6 (86%) | 52 (95%) | |
| Undifferentiated carcinoma | 3 (3.1%) | 1 (14%) | 0 (0%) | |
| Stage | 0.4 | |||
| I/II | 49 (51%) | 2 (29%) | 32 (58%) | |
| III/IVA/IVB | 45 (46%) | 5 (71%) | 21 (38%) | |
| IVC | 3 (3.1%) | 0 (0%) | 2 (3.6%) | |
| p16.status | 0.090 | |||
| Negative | 43 (44%) | 5 (71%) | 18 (33%) | |
| Positive | 54 (56%) | 2 (29%) | 37 (67%) | |
| Treatment.Group | 0.2 | |||
| Definitive CRT or RT | 69 (71%) | 5 (71%) | 51 (93%) | |
| None (Declined Treatment) | 1 (1.0%) | 0 (0%) | 0 (0%) | |
| None (Hospice) | 2 (2.1%) | 0 (0%) | 0 (0%) | |
| Surgery + CRT or RT | 24 (25%) | 2 (29%) | 3 (5.5%) | |
| Surgery only | 1 (1.0%) | 0 (0%) | 1 (1.8%) | |
| PFS.Event | 0.4 | |||
| No Progression | 65 (67%) | 6 (86%) | 35 (64%) | |
| Progression | 32 (33%) | 1 (14%) | 20 (36%) | |
| OS.Event | 0.3 | |||
| Alive | 81 (84%) | 7 (100%) | 44 (80%) | |
| Deceased | 16 (16%) | 0 (0%) | 11 (20%) | |
| OS.months | 22 (2 - 56) | 31 (21 - 39) | 16 (2 - 45) | 0.028 |
| 1 n (%); Median (Min - Max) | ||||
| 2 Fisher’s exact test; Wilcoxon rank sum test | ||||
fit1 <- as_flex_table(
merged_table,
include = everything(),
return_calls = FALSE
)
fit1
| Table 1 | Table 2 | ||
|---|---|---|---|---|
Characteristic | N = 971 | Negative | Positive | p-value2 |
Sex | 0.10 | |||
Female | 17 (18%) | 3 (43%) | 8 (15%) | |
Male | 80 (82%) | 4 (57%) | 47 (85%) | |
Age | 66 (29 - 95) | 80 (53 - 95) | 65 (37 - 95) | 0.081 |
Tobacco.History | 63 (65%) | 4 (57%) | 37 (67%) | 0.7 |
Prim.Location | 0.037 | |||
Larynx/Hypopharynx | 5 (5.2%) | 0 (0%) | 3 (5.5%) | |
Oral cavity | 16 (16%) | 3 (43%) | 4 (7.3%) | |
Oropharynx | 67 (69%) | 3 (43%) | 44 (80%) | |
Other (paranasal sinus and nasopharyngeal) | 9 (9.3%) | 1 (14%) | 4 (7.3%) | |
cT | 0.050 | |||
T0 | 2 (2.1%) | 0 (0%) | 2 (3.6%) | |
T1 | 12 (12%) | 1 (14%) | 4 (7.3%) | |
T2 | 31 (32%) | 2 (29%) | 19 (35%) | |
T3 | 30 (31%) | 0 (0%) | 21 (38%) | |
T4 | 21 (22%) | 4 (57%) | 9 (16%) | |
TX | 1 (1.0%) | 0 (0%) | 0 (0%) | |
cN | >0.9 | |||
N0 | 22 (23%) | 1 (14%) | 11 (20%) | |
N1 | 33 (34%) | 3 (43%) | 19 (35%) | |
N2 | 33 (34%) | 3 (43%) | 19 (35%) | |
N3 | 9 (9.3%) | 0 (0%) | 6 (11%) | |
cM | >0.9 | |||
M0 | 93 (96%) | 7 (100%) | 53 (96%) | |
M1 | 4 (4.1%) | 0 (0%) | 2 (3.6%) | |
Histology | 0.14 | |||
Adenosquamous carcinoma | 1 (1.0%) | 0 (0%) | 0 (0%) | |
Basaloid squamous cell carcinoma | 6 (6.2%) | 0 (0%) | 3 (5.5%) | |
Epithelial myoepithelial carcinoma | 1 (1.0%) | 0 (0%) | 0 (0%) | |
Squamous cell carcinoma | 86 (89%) | 6 (86%) | 52 (95%) | |
Undifferentiated carcinoma | 3 (3.1%) | 1 (14%) | 0 (0%) | |
Stage | 0.4 | |||
I/II | 49 (51%) | 2 (29%) | 32 (58%) | |
III/IVA/IVB | 45 (46%) | 5 (71%) | 21 (38%) | |
IVC | 3 (3.1%) | 0 (0%) | 2 (3.6%) | |
p16.status | 0.090 | |||
Negative | 43 (44%) | 5 (71%) | 18 (33%) | |
Positive | 54 (56%) | 2 (29%) | 37 (67%) | |
Treatment.Group | 0.2 | |||
Definitive CRT or RT | 69 (71%) | 5 (71%) | 51 (93%) | |
None (Declined Treatment) | 1 (1.0%) | 0 (0%) | 0 (0%) | |
None (Hospice) | 2 (2.1%) | 0 (0%) | 0 (0%) | |
Surgery + CRT or RT | 24 (25%) | 2 (29%) | 3 (5.5%) | |
Surgery only | 1 (1.0%) | 0 (0%) | 1 (1.8%) | |
PFS.Event | 0.4 | |||
No Progression | 65 (67%) | 6 (86%) | 35 (64%) | |
Progression | 32 (33%) | 1 (14%) | 20 (36%) | |
OS.Event | 0.3 | |||
Alive | 81 (84%) | 7 (100%) | 44 (80%) | |
Deceased | 16 (16%) | 0 (0%) | 11 (20%) | |
OS.months | 22 (2 - 56) | 31 (21 - 39) | 16 (2 - 45) | 0.028 |
1n (%); Median (Min - Max) | ||||
2Fisher's exact test; Wilcoxon rank sum test | ||||
save_as_docx(fit1, path = "~/Downloads/1b. CLIA HNSCC UNM Demographics Table by ctDNA.docx")#Overview plot by Stage
setwd("~/Downloads")
clinstage <- read.csv("CLIA HNSCC UNM_OP.csv")
clinstage_df <- as.data.frame(clinstage)
# Creating the basic swimmer plot
oplot <- swimmer_plot(df=clinstage_df,
id='PatientName',
end='fu.diff.months',
fill='gray',
width=.01,
base_size = 14,
stratify= c('Stage'))
# Adding themes and scales
oplot <- oplot + theme(panel.border = element_blank())
oplot <- oplot + scale_y_continuous(breaks = seq(0, 72, by = 3))
oplot <- oplot + labs(x ="Patients", y="Months from Diagnosis")
# Adding swimmer points
oplot_ev1 <- oplot + swimmer_points(df_points=clinstage_df,
id='PatientName',
time='date.diff.months',
name_shape ='Event_type',
name_col = 'Event',
size=3.5,fill='black')
# Optionally uncomment and use col='darkgreen' if needed
# Adding shape manual scale
oplot_ev1.1 <- oplot_ev1 + ggplot2::scale_shape_manual(name="Event_type",
values=c(1,16,6,18,18,4),
breaks=c('ctDNA_neg','ctDNA_pos', 'Imaging','Surgery','Biopsy', 'Death'))
# Display the plot
oplot_ev1.1
oplot_ev2 <- oplot_ev1.1 + swimmer_lines(df_lines=clinstage_df,
id='PatientName',
start='Tx_start.months',
end='Tx_end.months',
name_col='Tx_type',
size=3.5,
name_alpha = 1.0)
oplot_ev2 <- oplot_ev2 + guides(linetype = guide_legend(override.aes = list(size = 5, color = "black")))
oplot_ev2
oplot_ev2.2 <- oplot_ev2 + ggplot2::scale_color_manual(name="Event",values=c( "grey", "orange", "black", "black", "green", "red", "purple", "blue"))
oplot_ev2.2
#PFS in Complete Cohort (N=97)
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
survfit(Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)~ctDNA.available, data = circ_data)Call: survfit(formula = Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event) ~
ctDNA.available, data = circ_data)
n events median 0.95LCL 0.95UCL
[1,] 97 32 NA NA NAsurv_object <-Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.available, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue"), title="PFS - Complete Cohort (n=97)", ylab= "Progression-Free Survival", xlab="Months from Start of definitive Treatment", legend.labs=c("Complete cohort"), legend.title="")
summary(KM_curve, times= c(12, 24, 36))Call: survfit(formula = surv_object ~ ctDNA.available, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 65 22 0.770 0.0432 0.672 0.842
24 36 8 0.660 0.0518 0.548 0.751
36 12 2 0.622 0.0556 0.503 0.720#OS in Complete Cohort (N=97)
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
survfit(Surv(time = circ_data$OS.months, event = circ_data$OS.Event)~ctDNA.available, data = circ_data)Call: survfit(formula = Surv(time = circ_data$OS.months, event = circ_data$OS.Event) ~
ctDNA.available, data = circ_data)
n events median 0.95LCL 0.95UCL
[1,] 97 16 NA NA NAsurv_object <-Surv(time = circ_data$OS.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.available, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue"), title="OS - Complete Cohort (n=97)", ylab= "Overall Survival", xlab="Months from Start of definitive Treatment", legend.labs=c("Complete cohort"), legend.title="")
summary(KM_curve, times= c(12, 24, 36))Call: survfit(formula = surv_object ~ ctDNA.available, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 73 13 0.866 0.0347 0.780 0.920
24 42 3 0.822 0.0412 0.724 0.888
36 17 0 0.822 0.0412 0.724 0.888#Association of Baseline ctDNA MTM levels with clinicopathological factors
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_datadf <- as.data.frame(circ_data)
tally(~cStage, data=circ_data, margins = TRUE)cStage
I/II III/IV Total
34 28 62 circ_data$cStage <- factor(circ_data$cStage, levels = c("I/II","III/IV"), labels = c("I/II (n=34)","III/IV (n=29)"))
boxplot(ctDNA.Base.MTM~cStage, data=circ_data, main="ctDNA pre-treatment MTM - Stage", xlab="Stage", ylab="MTM/mL", col="white",border="black", ylim = c(0, 200))
median_ctDNA.Stage <- circ_data %>%
group_by(cStage) %>%
summarise(median_ctDNA_Base_MTM = median(ctDNA.Base.MTM, na.rm = TRUE))
print(median_ctDNA.Stage)m1<-wilcox.test(ctDNA.Base.MTM ~ cStage, data=circ_data, na.rm=TRUE, exact=FALSE, conf.int=TRUE)
print(m1)
Wilcoxon rank sum test with continuity correction
data: ctDNA.Base.MTM by cStage
W = 590, p-value = 0.1081
alternative hypothesis: true location shift is not equal to 0
95 percent confidence interval:
-0.7499638 20.1599876
sample estimates:
difference in location
3.690372 rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_datadf <- as.data.frame(circ_data)
tally(~cT.status, data=circ_data, margins = TRUE)cT.status
T0-T2 T3-T4 Total
28 34 62 circ_data$cT.status <- factor(circ_data$cT.status, levels = c("T0-T2","T3-T4"), labels = c("T0-T2 (n=28)","T3-T4 (n=34)"))
boxplot(ctDNA.Base.MTM~cT.status, data=circ_data, main="ctDNA pre-treatment MTM - T stage", xlab="T stage", ylab="MTM/mL", col="white",border="black", ylim = c(0, 200))
median_ctDNA.cT <- circ_data %>%
group_by(cT.status) %>%
summarise(median_ctDNA_Base_MTM = median(ctDNA.Base.MTM, na.rm = TRUE))
print(median_ctDNA.cT)m2<-wilcox.test(ctDNA.Base.MTM ~ cT.status, data=circ_data, na.rm=TRUE, exact=FALSE, conf.int=TRUE)
print(m2)
Wilcoxon rank sum test with continuity correction
data: ctDNA.Base.MTM by cT.status
W = 466, p-value = 0.893
alternative hypothesis: true location shift is not equal to 0
95 percent confidence interval:
-7.789979 7.539938
sample estimates:
difference in location
-0.1111266 rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_datadf <- as.data.frame(circ_data)
tally(~cT, data=circ_data, margins = TRUE)cT
T0 T1 T2 T3 T4 Total
2 5 21 21 13 62 circ_data$cT <- factor(circ_data$cT, levels = c("T0","T1","T2","T3","T4"))
boxplot(ctDNA.Base.MTM~cT, data=circ_data, main="ctDNA pre-treatment MTM - cT status", xlab="cT status", ylab="MTM/mL", col="white",border="black", ylim = c(0, 200))
median_ctDNA.cT <- circ_data %>%
group_by(cT) %>%
summarise(median_ctDNA_Base_MTM = median(ctDNA.Base.MTM, na.rm = TRUE))
print(median_ctDNA.cT)pairwise_wilcox <- pairwise.wilcox.test(circ_data$ctDNA.Base.MTM, circ_data$cT,
p.adjust.method = "none",
exact = FALSE)
print(pairwise_wilcox)
Pairwise comparisons using Wilcoxon rank sum test with continuity correction
data: circ_data$ctDNA.Base.MTM and circ_data$cT
T0 T1 T2 T3
T1 0.85 - - -
T2 0.21 0.85 - -
T3 0.14 0.65 0.69 -
T4 0.55 0.62 0.36 0.23
P value adjustment method: none rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available == "TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Base != "",]
circ_data$cT <- factor(circ_data$cT, levels = c("T0", "T1", "T2", "T3", "T4"))
circ_data$ctDNA.Base.MTM <- as.numeric(circ_data$ctDNA.Base.MTM)
cT_levels <- levels(circ_data$cT)
p_value_matrix <- matrix(NA, nrow = length(cT_levels), ncol = length(cT_levels))
rownames(p_value_matrix) <- cT_levels
colnames(p_value_matrix) <- cT_levels
for (i in 1:length(cT_levels)) {
for (j in i:length(cT_levels)) {
if (i != j) {
# Extract data for both groups
data1 <- circ_data %>% filter(cT == cT_levels[i]) %>% pull(ctDNA.Base.MTM)
data2 <- circ_data %>% filter(cT == cT_levels[j]) %>% pull(ctDNA.Base.MTM)
# Perform Wilcoxon test and store p-value
test_result <- wilcox.test(data1, data2, exact = FALSE)
p_value_matrix[i, j] <- test_result$p.value
p_value_matrix[j, i] <- test_result$p.value # Make symmetric
} else {
p_value_matrix[i, j] <- 1 # Self-comparison = 1
}
}
}
p_value_matrix[is.na(p_value_matrix)] <- 1.00
p_value_data <- melt(p_value_matrix)
colnames(p_value_data) <- c("cT1", "cT2", "p_value")
p_value_data <- p_value_data %>%
mutate(
significance = case_when(
p_value < 0.001 ~ "***",
p_value < 0.01 ~ "**",
p_value < 0.05 ~ "*",
TRUE ~ ""
)
)
ggplot(p_value_data, aes(x = cT1, y = cT2, fill = p_value)) +
geom_tile(color = "white", size = 0.8) + # Thicker grid lines for separation
geom_text(aes(label = significance), color = "black", size = 6, fontface = "bold") + # Significance markers
scale_fill_gradient2(low = "blue", mid = "white", high = "red", midpoint = 0.05) + # Gradient colors
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 12, face = "bold"),
axis.text.y = element_text(size = 12, face = "bold"),
panel.grid = element_blank()) +
labs(title = "Pairwise Wilcoxon-Test P-Values (ctDNA.Base.MTM by cT)",
x = "cT Status", y = "cT Status", fill = "P-Value")G2;H2;Warningh: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.
This warning is displayed once every 8 hours.
Call `lifecycle::last_lifecycle_]8;;ide:run:warnings()warnings()]8;;` to see where this warning was generated.g
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_datadf <- as.data.frame(circ_data)
tally(~cN.status, data=circ_data, margins = TRUE)cN.status
N0 N1-N3 Total
12 50 62 circ_data$cN.status <- factor(circ_data$cN.status, levels = c("N0","N1-N3"), labels = c("N0 (n=12)","N1-N3 (n=50)"))
boxplot(ctDNA.Base.MTM~cN.status, data=circ_data, main="ctDNA pre-treatment MTM - cN status", xlab="cN status", ylab="MTM/mL", col="white",border="black", ylim = c(0, 200))
median_ctDNA.cN <- circ_data %>%
group_by(cN.status) %>%
summarise(median_ctDNA_Base_MTM = median(ctDNA.Base.MTM, na.rm = TRUE))
print(median_ctDNA.cN)m3<-wilcox.test(ctDNA.Base.MTM ~ cN.status, data=circ_data, na.rm=TRUE, exact=FALSE, conf.int=TRUE)
print(m3)
Wilcoxon rank sum test with continuity correction
data: ctDNA.Base.MTM by cN.status
W = 162, p-value = 0.01422
alternative hypothesis: true location shift is not equal to 0
95 percent confidence interval:
-42.439959 -1.050045
sample estimates:
difference in location
-11.19909 rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_datadf <- as.data.frame(circ_data)
tally(~cN, data=circ_data, margins = TRUE)cN
N0 N1 N2 N3 Total
12 22 22 6 62 circ_data$cN <- factor(circ_data$cN, levels = c("N0","N1","N2","N3"))
boxplot(ctDNA.Base.MTM~cN, data=circ_data, main="ctDNA pre-treatment MTM - N Stage", xlab="N Stage", ylab="MTM/mL", col="white",border="black", ylim = c(0, 500))
median_ctDNA.cN <- circ_data %>%
group_by(cN) %>%
summarise(median_ctDNA_Base_MTM = median(ctDNA.Base.MTM, na.rm = TRUE))
print(median_ctDNA.cN)pairwise_wilcox <- pairwise.wilcox.test(circ_data$ctDNA.Base.MTM, circ_data$cN,
p.adjust.method = "none",
exact = FALSE)
print(pairwise_wilcox)
Pairwise comparisons using Wilcoxon rank sum test with continuity correction
data: circ_data$ctDNA.Base.MTM and circ_data$cN
N0 N1 N2
N1 0.0473 - -
N2 0.0094 0.4108 -
N3 0.3736 0.9777 0.7580
P value adjustment method: none rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available == "TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Base != "",]
circ_data$cN <- factor(circ_data$cN, levels = c("N0","N1","N2","N3"))
circ_data$ctDNA.Base.MTM <- as.numeric(circ_data$ctDNA.Base.MTM)
cN_levels <- levels(circ_data$cN)
p_value_matrix <- matrix(NA, nrow = length(cN_levels), ncol = length(cN_levels))
rownames(p_value_matrix) <- cN_levels
colnames(p_value_matrix) <- cN_levels
for (i in 1:length(cN_levels)) {
for (j in i:length(cN_levels)) {
if (i != j) {
# Extract data for both groups
data1 <- circ_data %>% filter(cN == cN_levels[i]) %>% pull(ctDNA.Base.MTM)
data2 <- circ_data %>% filter(cN == cN_levels[j]) %>% pull(ctDNA.Base.MTM)
# Perform Wilcoxon test and store p-value
test_result <- wilcox.test(data1, data2, exact = FALSE)
p_value_matrix[i, j] <- test_result$p.value
p_value_matrix[j, i] <- test_result$p.value # Make symmetric
} else {
p_value_matrix[i, j] <- 1 # Self-comparison = 1
}
}
}
p_value_matrix[is.na(p_value_matrix)] <- 1.00
p_value_data <- melt(p_value_matrix)
colnames(p_value_data) <- c("cN1", "cN2", "p_value")
p_value_data <- p_value_data %>%
mutate(
significance = case_when(
p_value < 0.001 ~ "***",
p_value < 0.01 ~ "**",
p_value < 0.05 ~ "*",
TRUE ~ ""
)
)
ggplot(p_value_data, aes(x = cN1, y = cN2, fill = p_value)) +
geom_tile(color = "white", size = 0.8) + # Thicker grid lines for separation
geom_text(aes(label = significance), color = "black", size = 6, fontface = "bold") + # Significance markers
scale_fill_gradient2(low = "blue", mid = "white", high = "red", midpoint = 0.05) + # Gradient colors
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 12, face = "bold"),
axis.text.y = element_text(size = 12, face = "bold"),
panel.grid = element_blank()) +
labs(title = "Pairwise Wilcoxon-Test P-Values (ctDNA.Base.MTM by cN)",
x = "Status", y = "cN Status", fill = "P-Value")
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available == "TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Base != "",]
circ_data$cT <- factor(circ_data$cT, levels = c("T0", "T1", "T2", "T3", "T4"))
circ_data$cN <- factor(circ_data$cN, levels = c("N0", "N1", "N2", "N3"))
circ_data$ctDNA.Base.MTM <- as.numeric(circ_data$ctDNA.Base.MTM)
median_ctDNA <- circ_data %>%
group_by(cT, cN) %>%
summarise(median_ctDNA_Base_MTM = median(ctDNA.Base.MTM, na.rm = TRUE)) %>%
ungroup()`summarise()` has grouped output by 'cT'. You can override using the `.groups` argument.p_value_matrix <- dcast(median_ctDNA, cT ~ cN, value.var = "median_ctDNA_Base_MTM")
p_value_data <- melt(p_value_matrix, id.vars = "cT", variable.name = "cN", value.name = "median_value")
p_value_data$missing <- ifelse(is.na(p_value_data$median_value), "Missing", "Present")
p_value_data$median_value[is.na(p_value_data$median_value)] <- 0
ggplot(p_value_data, aes(x = cN, y = cT, fill = median_value)) +
geom_tile(color = "black", size = 0.5) + # Black gridlines for separation
geom_text(aes(label = round(median_value, 2)), color = "black", size = 5) + # Display median values
scale_fill_gradient(low = "white", high = "blue") + # Color gradient similar to the reference image
theme_minimal() +
theme(axis.text.x = element_text(size = 12, face = "bold"),
axis.text.y = element_text(size = 12, face = "bold"),
panel.grid = element_blank()) +
labs(title = "Median ctDNA.Base.MTM by cT and cN",
x = "cN Status", y = "cT Status", fill = "Median MTM") +
geom_tile(data = subset(p_value_data, missing == "Missing"),
aes(x = cN, y = cT), color = "black", fill = NA, size = 0.5, linetype = "dashed") # Add diagonal cross for missing values
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_datadf <- as.data.frame(circ_data)
tally(~cM, data=circ_data, margins = TRUE)cM
M0 M1 Total
60 2 62 circ_data$cM <- factor(circ_data$cM, levels = c("M0","M1"), labels = c("M0 (n=60)","M1 (n=2)"))
boxplot(ctDNA.Base.MTM~cM, data=circ_data, main="ctDNA pre-treatment MTM - cM", xlab="cM", ylab="MTM/mL", col="white",border="black", ylim = c(0, 500))
median_ctDNA.cM <- circ_data %>%
group_by(cM) %>%
summarise(median_ctDNA_Base_MTM = median(ctDNA.Base.MTM, na.rm = TRUE))
print(median_ctDNA.cM)m4<-wilcox.test(ctDNA.Base.MTM ~ cM, data=circ_data, na.rm=TRUE, exact=FALSE, conf.int=TRUE)
print(m4)
Wilcoxon rank sum test with continuity correction
data: ctDNA.Base.MTM by cM
W = 53, p-value = 0.7955
alternative hypothesis: true location shift is not equal to 0
95 percent confidence interval:
-469.98001 76.11002
sample estimates:
difference in location
-0.07005722 rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_datadf <- as.data.frame(circ_data)
tally(~p16.status, data=circ_data, margins = TRUE)p16.status
Negative Positive Total
23 39 62 circ_data$p16.status <- factor(circ_data$p16.status, levels = c("Negative","Positive"), labels = c("p16 neg (n=23)","p16 pos (n=39)"))
boxplot(ctDNA.Base.MTM~p16.status, data=circ_data, main="ctDNA pre-treatment MTM - p16 status", xlab="p16 status", ylab="MTM/mL", col="white",border="black", ylim = c(0, 200))
median_ctDNA.p16 <- circ_data %>%
group_by(p16.status) %>%
summarise(median_ctDNA_Base_MTM = median(ctDNA.Base.MTM, na.rm = TRUE))
print(median_ctDNA.p16)m5<-wilcox.test(ctDNA.Base.MTM ~ p16.status, data=circ_data, na.rm=TRUE, exact=FALSE, conf.int=TRUE)
print(m5)
Wilcoxon rank sum test with continuity correction
data: ctDNA.Base.MTM by p16.status
W = 269, p-value = 0.009047
alternative hypothesis: true location shift is not equal to 0
95 percent confidence interval:
-27.889950 -1.040095
sample estimates:
difference in location
-8.739937 rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_datadf <- as.data.frame(circ_data)
tally(~Prim.Location, data=circ_data, margins = TRUE)Prim.Location
Larynx/Hypopharynx Oral cavity Oropharynx Other (paranasal sinus and nasopharyngeal)
3 7 47 5
Total
62 circ_data$Prim.Location <- factor(circ_data$Prim.Location, levels = c("Larynx/Hypopharynx","Oral cavity", "Oropharynx", "Other (paranasal sinus and nasopharyngeal)"))
boxplot(ctDNA.Base.MTM~Prim.Location, data=circ_data, main="ctDNA pre-treatment MTM - Tumor Location", xlab="Tumor Location", ylab="MTM/mL", col="white",border="black", ylim = c(0, 200))
median_ctDNA.loc <- circ_data %>%
group_by(Prim.Location) %>%
summarise(median_ctDNA_Base_MTM = median(ctDNA.Base.MTM, na.rm = TRUE))
print(median_ctDNA.loc)pairwise_wilcox <- pairwise.wilcox.test(circ_data$ctDNA.Base.MTM, circ_data$Prim.Location,
p.adjust.method = "none",
exact = FALSE)
print(pairwise_wilcox)
Pairwise comparisons using Wilcoxon rank sum test with continuity correction
data: circ_data$ctDNA.Base.MTM and circ_data$Prim.Location
Larynx/Hypopharynx Oral cavity Oropharynx
Oral cavity 0.644 - -
Oropharynx 0.253 0.065 -
Other (paranasal sinus and nasopharyngeal) 0.551 0.563 0.653
P value adjustment method: none rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available == "TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Base != "",]
circ_data$Prim.Location <- factor(circ_data$Prim.Location, levels = c("Larynx/Hypopharynx","Oral cavity", "Oropharynx", "Other (paranasal sinus and nasopharyngeal)"), labels = c("LRX/HPRX","OC","PRX","Other"))
circ_data$ctDNA.Base.MTM <- as.numeric(circ_data$ctDNA.Base.MTM)
pl_levels <- levels(circ_data$Prim.Location)
p_value_matrix <- matrix(NA, nrow = length(pl_levels), ncol = length(pl_levels))
rownames(p_value_matrix) <- pl_levels
colnames(p_value_matrix) <- pl_levels
for (i in 1:length(pl_levels)) {
for (j in i:length(pl_levels)) {
if (i != j) {
# Extract data for both groups
data1 <- circ_data %>% filter(Prim.Location == pl_levels[i]) %>% pull(ctDNA.Base.MTM)
data2 <- circ_data %>% filter(Prim.Location == pl_levels[j]) %>% pull(ctDNA.Base.MTM)
# Perform Wilcoxon test and store p-value
test_result <- wilcox.test(data1, data2, exact = FALSE)
p_value_matrix[i, j] <- test_result$p.value
p_value_matrix[j, i] <- test_result$p.value # Make symmetric
} else {
p_value_matrix[i, j] <- 1 # Self-comparison = 1
}
}
}
p_value_matrix[is.na(p_value_matrix)] <- 1.00
p_value_data <- melt(p_value_matrix)
colnames(p_value_data) <- c("pl1", "pl2", "p_value")
p_value_data <- p_value_data %>%
mutate(
significance = case_when(
p_value < 0.001 ~ "***",
p_value < 0.01 ~ "**",
p_value < 0.05 ~ "*",
TRUE ~ ""
)
)
ggplot(p_value_data, aes(x = pl1, y = pl2, fill = p_value)) +
geom_tile(color = "white", size = 0.8) + # Thicker grid lines for separation
geom_text(aes(label = significance), color = "black", size = 6, fontface = "bold") + # Significance markers
scale_fill_gradient2(low = "blue", mid = "white", high = "red", midpoint = 0.05) + # Gradient colors
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 12, face = "bold"),
axis.text.y = element_text(size = 12, face = "bold"),
panel.grid = element_blank()) +
labs(title = "Pairwise Wilcoxon-Test P-Values (ctDNA.Base.MTM by Tumor Location)",
x = "Tumor Location", y = "Tumor Location", fill = "P-Value")
#PFS by ctDNA status at MRD
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)~ctDNA.MRD, data = circ_data)Call: survfit(formula = Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event) ~
ctDNA.MRD, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.MRD=NEGATIVE 56 7 NA NA NA
ctDNA.MRD=POSITIVE 13 8 15.5 4.21 NAevent_summary <- circ_data %>%
group_by(ctDNA.MRD) %>%
summarise(
Total = n(),
Events = sum(PFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="PFS - ctDNA at MRD", ylab= "Progression-Free Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 12, 24, 36))Call: survfit(formula = surv_object ~ ctDNA.MRD, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.MRD=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 56 0 1.000 0.0000 1.000 1.000
12 45 4 0.928 0.0348 0.819 0.972
24 27 3 0.859 0.0503 0.723 0.931
36 8 0 0.859 0.0503 0.723 0.931
ctDNA.MRD=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 13 0 1.000 0.000 1.0000 1.000
12 6 6 0.538 0.138 0.2477 0.760
24 2 2 0.323 0.144 0.0862 0.594
36 2 0 0.323 0.144 0.0862 0.594circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.MRD, data = circ_data)
n= 69, number of events= 15
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.MRDPOSITIVE 1.995 7.349 0.520 3.836 0.000125 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.MRDPOSITIVE 7.349 0.1361 2.652 20.36
Concordance= 0.709 (se = 0.061 )
Likelihood ratio test= 13.29 on 1 df, p=3e-04
Wald test = 14.71 on 1 df, p=1e-04
Score (logrank) test = 20.16 on 1 df, p=7e-06cox_fit_summary <- summary(cox_fit)
#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 7.35 (2.65-20.36); p = 0"circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PFS.Event <- factor(circ_data$PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression"))
contingency_table <- table(circ_data$ctDNA.MRD, circ_data$PFS.Event)
chi_square_test <- chisq.test(contingency_table)G2;H2;Warningh in stats::chisq.test(x, y, ...) :
Chi-squared approximation may be incorrectgprint(chi_square_test)
Pearson's Chi-squared test with Yates' continuity correction
data: contingency_table
X-squared = 12.17, df = 1, p-value = 0.0004856fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
Fisher's Exact Test for Count Data
data: contingency_table
p-value = 0.0005695
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
2.336708 55.181495
sample estimates:
odds ratio
10.61815 print(contingency_table)
No Progression Progression
Negative 49 7
Positive 5 8table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "ctDNA status at MRD",
x = "ctDNA",
y = "Patients (%)",
fill = "Progression",
caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("No Progression" = "blue", "Progression" = "red")) + # define custom colors
theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Progression label size
#OS by ctDNA status at MRD
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$OS.months, event = circ_data$OS.Event)~ctDNA.MRD, data = circ_data)Call: survfit(formula = Surv(time = circ_data$OS.months, event = circ_data$OS.Event) ~
ctDNA.MRD, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.MRD=NEGATIVE 56 1 NA NA NA
ctDNA.MRD=POSITIVE 13 5 NA 12.3 NAevent_summary <- circ_data %>%
group_by(ctDNA.MRD) %>%
summarise(
Total = n(),
Events = sum(OS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$OS.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="OS - ctDNA at MRD", ylab= "Overall Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(12, 24, 36))Call: survfit(formula = surv_object ~ ctDNA.MRD, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.MRD=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 48 1 0.982 0.0177 0.88 0.997
24 30 0 0.982 0.0177 0.88 0.997
36 10 0 0.982 0.0177 0.88 0.997
ctDNA.MRD=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 8 3 0.769 0.117 0.442 0.919
24 4 2 0.561 0.153 0.233 0.795
36 3 0 0.561 0.153 0.233 0.795circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.MRD, data = circ_data)
n= 69, number of events= 6
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.MRDPOSITIVE 3.247 25.718 1.096 2.962 0.00306 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.MRDPOSITIVE 25.72 0.03888 3 220.5
Concordance= 0.832 (se = 0.081 )
Likelihood ratio test= 13.07 on 1 df, p=3e-04
Wald test = 8.77 on 1 df, p=0.003
Score (logrank) test = 19.67 on 1 df, p=9e-06cox_fit_summary <- summary(cox_fit)
#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 25.72 (3-220.48); p = 0.003"circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$OS.Event <- factor(circ_data$OS.Event, levels = c("FALSE", "TRUE"), labels = c("Alive", "Deceased"))
contingency_table <- table(circ_data$ctDNA.MRD, circ_data$OS.Event)
chi_square_test <- chisq.test(contingency_table)G2;H2;Warningh in stats::chisq.test(x, y, ...) :
Chi-squared approximation may be incorrectgprint(chi_square_test)
Pearson's Chi-squared test with Yates' continuity correction
data: contingency_table
X-squared = 13.554, df = 1, p-value = 0.0002318fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
Fisher's Exact Test for Count Data
data: contingency_table
p-value = 0.0006155
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
3.015475 1634.641331
sample estimates:
odds ratio
31.44433 print(contingency_table)
Alive Deceased
Negative 55 1
Positive 8 5table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "ctDNA status at MRD",
x = "ctDNA",
y = "Patients (%)",
fill = "Living Status",
caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("Alive" = "blue", "Deceased" = "red")) + # define custom colors
theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Progression label size
#PFS by ctDNA status at MRD - exclude pts with no subsequent adj. treatment
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[!(circ_data$Surgery == TRUE & circ_data$Chemotherapy == FALSE), ]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)~ctDNA.MRD, data = circ_data)Call: survfit(formula = Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event) ~
ctDNA.MRD, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.MRD=NEGATIVE 48 6 NA NA NA
ctDNA.MRD=POSITIVE 10 7 8.67 3.12 NAevent_summary <- circ_data %>%
group_by(ctDNA.MRD) %>%
summarise(
Total = n(),
Events = sum(PFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="PFS - ctDNA at MRD", ylab= "Progression-Free Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 12, 24, 36))Call: survfit(formula = surv_object ~ ctDNA.MRD, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.MRD=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 48 0 1.000 0.0000 1.000 1.000
12 39 4 0.916 0.0404 0.791 0.968
24 24 2 0.866 0.0512 0.725 0.938
36 7 0 0.866 0.0512 0.725 0.938
ctDNA.MRD=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 10 0 1.000 0.000 1.0000 1.000
12 3 6 0.400 0.155 0.1227 0.670
24 2 1 0.267 0.150 0.0476 0.563
36 2 0 0.267 0.150 0.0476 0.563circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.MRD, data = circ_data)
n= 58, number of events= 13
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.MRDPOSITIVE 2.2507 9.4944 0.5614 4.009 6.1e-05 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.MRDPOSITIVE 9.494 0.1053 3.159 28.53
Concordance= 0.725 (se = 0.064 )
Likelihood ratio test= 14.24 on 1 df, p=2e-04
Wald test = 16.07 on 1 df, p=6e-05
Score (logrank) test = 23.76 on 1 df, p=1e-06cox_fit_summary <- summary(cox_fit)
#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 9.49 (3.16-28.53); p = 0"#PFS by ctDNA status at MRD Stage I/II
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$cStage=="I/II",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)~ctDNA.MRD, data = circ_data)Call: survfit(formula = Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event) ~
ctDNA.MRD, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.MRD=NEGATIVE 29 2 NA NA NA
ctDNA.MRD=POSITIVE 5 2 NA 6.01 NAevent_summary <- circ_data %>%
group_by(ctDNA.MRD) %>%
summarise(
Total = n(),
Events = sum(PFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="PFS - ctDNA at MRD Stage I/II", ylab= "Progression-Free Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 12, 24, 36))Call: survfit(formula = surv_object ~ ctDNA.MRD, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.MRD=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 29 0 1.000 0.0000 1.000 1.000
12 25 1 0.966 0.0339 0.779 0.995
24 16 1 0.925 0.0510 0.732 0.981
36 3 0 0.925 0.0510 0.732 0.981
ctDNA.MRD=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 5 0 1.0 0.000 1.000 1.000
12 2 2 0.6 0.219 0.126 0.882
24 1 0 0.6 0.219 0.126 0.882
36 1 0 0.6 0.219 0.126 0.882circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.MRD, data = circ_data)
n= 34, number of events= 4
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.MRDPOSITIVE 2.286 9.838 1.035 2.209 0.0272 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.MRDPOSITIVE 9.838 0.1016 1.294 74.78
Concordance= 0.725 (se = 0.118 )
Likelihood ratio test= 4.21 on 1 df, p=0.04
Wald test = 4.88 on 1 df, p=0.03
Score (logrank) test = 7.19 on 1 df, p=0.007cox_fit_summary <- summary(cox_fit)
#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 9.84 (1.29-74.78); p = 0.027"circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PFS.Event <- factor(circ_data$PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression"))
contingency_table <- table(circ_data$ctDNA.MRD, circ_data$PFS.Event)
chi_square_test <- chisq.test(contingency_table)G2;H2;Warningh in stats::chisq.test(x, y, ...) :
Chi-squared approximation may be incorrectgprint(chi_square_test)
Pearson's Chi-squared test with Yates' continuity correction
data: contingency_table
X-squared = 1.8778, df = 1, p-value = 0.1706fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
Fisher's Exact Test for Count Data
data: contingency_table
p-value = 0.09391
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.4412663 153.9655852
sample estimates:
odds ratio
8.070894 print(contingency_table)
No Progression Progression
Negative 27 2
Positive 3 2table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "ctDNA status at MRD Stage I/II",
x = "ctDNA",
y = "Patients (%)",
fill = "Progression",
caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("No Progression" = "blue", "Progression" = "red")) + # define custom colors
theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Progression label size
#PFS by ctDNA status at MRD Stage III/IV
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$cStage=="III/IV",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)~ctDNA.MRD, data = circ_data)Call: survfit(formula = Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event) ~
ctDNA.MRD, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.MRD=NEGATIVE 27 5 NA NA NA
ctDNA.MRD=POSITIVE 8 6 13.4 4.21 NAevent_summary <- circ_data %>%
group_by(ctDNA.MRD) %>%
summarise(
Total = n(),
Events = sum(PFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="PFS - ctDNA at MRD Stage III/IV", ylab= "Progression-Free Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 12, 24, 36))Call: survfit(formula = surv_object ~ ctDNA.MRD, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.MRD=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 27 0 1.000 0.0000 1.000 1.000
12 20 3 0.887 0.0613 0.690 0.962
24 11 2 0.788 0.0858 0.558 0.907
36 5 0 0.788 0.0858 0.558 0.907
ctDNA.MRD=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 8 0 1.00 0.000 1.0000 1.000
12 4 4 0.50 0.177 0.1520 0.775
24 1 2 0.25 0.153 0.0371 0.558
36 1 0 0.25 0.153 0.0371 0.558circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.MRD, data = circ_data)
n= 35, number of events= 11
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.MRDPOSITIVE 1.681 5.371 0.607 2.769 0.00562 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.MRDPOSITIVE 5.371 0.1862 1.634 17.65
Concordance= 0.69 (se = 0.072 )
Likelihood ratio test= 7.23 on 1 df, p=0.007
Wald test = 7.67 on 1 df, p=0.006
Score (logrank) test = 9.63 on 1 df, p=0.002cox_fit_summary <- summary(cox_fit)
#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 5.37 (1.63-17.65); p = 0.006"circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PFS.Event <- factor(circ_data$PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression"))
contingency_table <- table(circ_data$ctDNA.MRD, circ_data$PFS.Event)
chi_square_test <- chisq.test(contingency_table)G2;H2;Warningh in stats::chisq.test(x, y, ...) :
Chi-squared approximation may be incorrectgprint(chi_square_test)
Pearson's Chi-squared test with Yates' continuity correction
data: contingency_table
X-squared = 6.7026, df = 1, p-value = 0.009627fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
Fisher's Exact Test for Count Data
data: contingency_table
p-value = 0.005761
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
1.572192 155.593667
sample estimates:
odds ratio
11.93406 print(contingency_table)
No Progression Progression
Negative 22 5
Positive 2 6table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "ctDNA status at MRD Stage I/II",
x = "ctDNA",
y = "Patients (%)",
fill = "Progression",
caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("No Progression" = "blue", "Progression" = "red")) + # define custom colors
theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Progression label size
#PFS by ctDNA at MRD p16(+)
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$p16.status=="Positive",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)~ctDNA.MRD, data = circ_data)Call: survfit(formula = Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event) ~
ctDNA.MRD, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.MRD=NEGATIVE 29 2 NA NA NA
ctDNA.MRD=POSITIVE 8 4 22.2 6.01 NAevent_summary <- circ_data %>%
group_by(ctDNA.MRD) %>%
summarise(
Total = n(),
Events = sum(PFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="PFS - ctDNA at MRD p16(+)", ylab= "Progression-Free Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 12, 24, 36))Call: survfit(formula = surv_object ~ ctDNA.MRD, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.MRD=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 29 0 1.000 0.0000 1.000 1.000
12 23 1 0.966 0.0339 0.779 0.995
24 15 1 0.920 0.0553 0.711 0.980
36 4 0 0.920 0.0553 0.711 0.980
ctDNA.MRD=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 8 0 1.000 0.000 1.000 1.000
12 4 3 0.625 0.171 0.229 0.861
24 2 1 0.417 0.205 0.072 0.747
36 2 0 0.417 0.205 0.072 0.747circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.MRD, data = circ_data)
n= 37, number of events= 6
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.MRDPOSITIVE 2.3218 10.1936 0.8706 2.667 0.00766 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.MRDPOSITIVE 10.19 0.0981 1.85 56.16
Concordance= 0.767 (se = 0.09 )
Likelihood ratio test= 7.47 on 1 df, p=0.006
Wald test = 7.11 on 1 df, p=0.008
Score (logrank) test = 10.81 on 1 df, p=0.001cox_fit_summary <- summary(cox_fit)
#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 10.19 (1.85-56.16); p = 0.008"circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PFS.Event <- factor(circ_data$PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression"))
contingency_table <- table(circ_data$ctDNA.MRD, circ_data$PFS.Event)
chi_square_test <- chisq.test(contingency_table)G2;H2;Warningh in stats::chisq.test(x, y, ...) :
Chi-squared approximation may be incorrectgprint(chi_square_test)
Pearson's Chi-squared test with Yates' continuity correction
data: contingency_table
X-squared = 5.6953, df = 1, p-value = 0.01701fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
Fisher's Exact Test for Count Data
data: contingency_table
p-value = 0.01294
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
1.281882 176.017338
sample estimates:
odds ratio
12.07276 print(contingency_table)
No Progression Progression
Negative 27 2
Positive 4 4table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "ctDNA status at MRD p16(+)",
x = "ctDNA",
y = "Patients (%)",
fill = "Progression",
caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("No Progression" = "blue", "Progression" = "red")) + # define custom colors
theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Progression label size
#PFS by ctDNA at MRD p16(-)
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$p16.status=="Negative",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)~ctDNA.MRD, data = circ_data)Call: survfit(formula = Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event) ~
ctDNA.MRD, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.MRD=NEGATIVE 27 5 NA NA NA
ctDNA.MRD=POSITIVE 5 4 11.3 4.21 NAevent_summary <- circ_data %>%
group_by(ctDNA.MRD) %>%
summarise(
Total = n(),
Events = sum(PFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="PFS - ctDNA at MRD p16(-)", ylab= "Progression-Free Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 12, 24, 36))Call: survfit(formula = surv_object ~ ctDNA.MRD, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.MRD=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 27 0 1.000 0.0000 1.000 1.000
12 22 3 0.887 0.0613 0.690 0.962
24 12 2 0.797 0.0821 0.576 0.911
36 4 0 0.797 0.0821 0.576 0.911
ctDNA.MRD=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 5 0 1.0 0.000 1.000 1.000
12 2 3 0.4 0.219 0.052 0.753circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.MRD, data = circ_data)
n= 32, number of events= 9
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.MRDPOSITIVE 1.9377 6.9431 0.6781 2.857 0.00427 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.MRDPOSITIVE 6.943 0.144 1.838 26.23
Concordance= 0.682 (se = 0.077 )
Likelihood ratio test= 6.8 on 1 df, p=0.009
Wald test = 8.16 on 1 df, p=0.004
Score (logrank) test = 10.96 on 1 df, p=9e-04cox_fit_summary <- summary(cox_fit)
#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 6.94 (1.84-26.23); p = 0.004"circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PFS.Event <- factor(circ_data$PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression"))
contingency_table <- table(circ_data$ctDNA.MRD, circ_data$PFS.Event)
chi_square_test <- chisq.test(contingency_table)G2;H2;Warningh in stats::chisq.test(x, y, ...) :
Chi-squared approximation may be incorrectgprint(chi_square_test)
Pearson's Chi-squared test with Yates' continuity correction
data: contingency_table
X-squared = 5.1404, df = 1, p-value = 0.02338fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
Fisher's Exact Test for Count Data
data: contingency_table
p-value = 0.01502
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
1.221317 898.357859
sample estimates:
odds ratio
15.55541 print(contingency_table)
No Progression Progression
Negative 22 5
Positive 1 4table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "ctDNA status at MRD p16(-)",
x = "ctDNA",
y = "Patients (%)",
fill = "Progression",
caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("No Progression" = "blue", "Progression" = "red")) + # define custom colors
theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Progression label size
#PFS by ctDNA status at surveillance
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)~ctDNA.Surveillance, data = circ_data)Call: survfit(formula = Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event) ~
ctDNA.Surveillance, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.Surveillance=NEGATIVE 51 3 NA NA NA
ctDNA.Surveillance=POSITIVE 17 13 15.5 11.6 NAevent_summary <- circ_data %>%
group_by(ctDNA.Surveillance) %>%
summarise(
Total = n(),
Events = sum(PFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Surveillance, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="PFS - ctDNA at Surveillance", ylab= "Progression-Free Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 12, 24, 36))Call: survfit(formula = surv_object ~ ctDNA.Surveillance, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.Surveillance=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 51 0 1.000 0.0000 1.000 1.000
12 40 2 0.961 0.0272 0.852 0.990
24 22 1 0.929 0.0410 0.788 0.977
36 8 0 0.929 0.0410 0.788 0.977
ctDNA.Surveillance=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 17 0 1.000 0.000 1.0000 1.000
12 11 6 0.647 0.116 0.3771 0.823
24 5 5 0.343 0.118 0.1348 0.565
36 1 2 0.206 0.103 0.0528 0.428circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.Surveillance, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.Surveillance, data = circ_data)
n= 68, number of events= 16
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.SurveillancePOSITIVE 2.792 16.317 0.642 4.349 1.37e-05 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.SurveillancePOSITIVE 16.32 0.06129 4.636 57.43
Concordance= 0.792 (se = 0.055 )
Likelihood ratio test= 26.39 on 1 df, p=3e-07
Wald test = 18.91 on 1 df, p=1e-05
Score (logrank) test = 34.52 on 1 df, p=4e-09cox_fit_summary <- summary(cox_fit)
#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 16.32 (4.64-57.43); p = 0"circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PFS.Event <- factor(circ_data$PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression"))
contingency_table <- table(circ_data$ctDNA.Surveillance, circ_data$PFS.Event)
chi_square_test <- chisq.test(contingency_table)G2;H2;Warningh in stats::chisq.test(x, y, ...) :
Chi-squared approximation may be incorrectgprint(chi_square_test)
Pearson's Chi-squared test with Yates' continuity correction
data: contingency_table
X-squared = 31.494, df = 1, p-value = 2.001e-08fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
Fisher's Exact Test for Count Data
data: contingency_table
p-value = 3.432e-08
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
8.523283 366.804741
sample estimates:
odds ratio
46.11116 print(contingency_table)
No Progression Progression
Negative 48 3
Positive 4 13table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "ctDNA status at Surveillance",
x = "ctDNA",
y = "Patients (%)",
fill = "Progression",
caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("No Progression" = "blue", "Progression" = "red")) + # define custom colors
theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Progression label size
#OS by ctDNA status at surveillance
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$OS.months, event = circ_data$OS.Event)~ctDNA.Surveillance, data = circ_data)Call: survfit(formula = Surv(time = circ_data$OS.months, event = circ_data$OS.Event) ~
ctDNA.Surveillance, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.Surveillance=NEGATIVE 51 1 NA NA NA
ctDNA.Surveillance=POSITIVE 17 2 NA NA NAevent_summary <- circ_data %>%
group_by(ctDNA.Surveillance) %>%
summarise(
Total = n(),
Events = sum(OS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$OS.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Surveillance, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="OS - ctDNA at Surveillance", ylab= "Overall Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(12, 24, 36))Call: survfit(formula = surv_object ~ ctDNA.Surveillance, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.Surveillance=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 41 1 0.98 0.0194 0.869 0.997
24 22 0 0.98 0.0194 0.869 0.997
36 8 0 0.98 0.0194 0.869 0.997
ctDNA.Surveillance=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 17 0 1.000 0.0000 1.000 1.000
24 9 2 0.871 0.0856 0.573 0.966
36 5 0 0.871 0.0856 0.573 0.966circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.Surveillance, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.Surveillance, data = circ_data)
n= 68, number of events= 3
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.SurveillancePOSITIVE 1.662 5.270 1.226 1.356 0.175
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.SurveillancePOSITIVE 5.27 0.1898 0.4767 58.25
Concordance= 0.669 (se = 0.143 )
Likelihood ratio test= 1.98 on 1 df, p=0.2
Wald test = 1.84 on 1 df, p=0.2
Score (logrank) test = 2.3 on 1 df, p=0.1cox_fit_summary <- summary(cox_fit)
#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 5.27 (0.48-58.25); p = 0.175"circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$OS.Event <- factor(circ_data$OS.Event, levels = c("FALSE", "TRUE"), labels = c("Alive", "Deceased"))
contingency_table <- table(circ_data$ctDNA.Surveillance, circ_data$OS.Event)
chi_square_test <- chisq.test(contingency_table)G2;H2;Warningh in stats::chisq.test(x, y, ...) :
Chi-squared approximation may be incorrectgprint(chi_square_test)
Pearson's Chi-squared test with Yates' continuity correction
data: contingency_table
X-squared = 1.0462, df = 1, p-value = 0.3064fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
Fisher's Exact Test for Count Data
data: contingency_table
p-value = 0.152
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.3148908 399.7731665
sample estimates:
odds ratio
6.433899 print(contingency_table)
Alive Deceased
Negative 50 1
Positive 15 2table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "ctDNA status at Surveillance",
x = "ctDNA",
y = "Patients (%)",
fill = "Living Status",
caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("Alive" = "blue", "Deceased" = "red")) + # define custom colors
theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Progression label size
#Median numbers of time points and lead time in the longitudinal setting
# Load the dataset
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_datadf <- as.data.frame(circ_data)
median_Nsurvtps <- median(circ_datadf$Nsurvtps, na.rm = TRUE)
min_Nsurvtps <- min(circ_datadf$Nsurvtps, na.rm = TRUE)
max_Nsurvtps <- max(circ_datadf$Nsurvtps, na.rm = TRUE)
cat(sprintf("Median # of surveillance time points: %d (%d-%d)\n",
median_Nsurvtps, min_Nsurvtps, max_Nsurvtps))Median # of surveillance time points: 4 (1-13)circ_datadf$LeadTime_Months <- circ_datadf$LeadTime / 30.437
median_LeadTime <- median(circ_datadf$LeadTime_Months, na.rm = TRUE)
min_LeadTime <- min(circ_datadf$LeadTime_Months, na.rm = TRUE)
max_LeadTime <- max(circ_datadf$LeadTime_Months, na.rm = TRUE)
cat(sprintf("Longitudinally, ctDNA positivity preceded progression by a median of %.2f mo (%.2f–%.2f)\n",
median_LeadTime, min_LeadTime, max_LeadTime))Longitudinally, ctDNA positivity preceded progression by a median of 4.75 mo (0.00–13.96)#Time-dependent analysis for PFS in longitudinal time points
rm(list=ls())
setwd("~/Downloads")
dt <- read_xlsx("CLIA HNSCC Peddada Clinical Data_Time dependent.xlsx") |>
clean_names() |>
mutate(across(.cols = c(window_start_date,dfs_date,
surveillance_1_date:surveillance_12_date),
.fns = ~ as_date(as.Date(.x, format = "%Y-%m-%d"))))G2;H2;Warningh: Expecting numeric in Z5 / R5C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z8 / R8C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z9 / R9C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z15 / R15C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z17 / R17C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z18 / R18C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z19 / R19C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z22 / R22C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z23 / R23C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z25 / R25C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z26 / R26C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z27 / R27C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z28 / R28C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z29 / R29C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z32 / R32C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z34 / R34C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z35 / R35C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z38 / R38C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z41 / R41C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z47 / R47C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z48 / R48C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z49 / R49C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z56 / R56C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z58 / R58C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z60 / R60C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z63 / R63C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z64 / R64C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z65 / R65C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z67 / R67C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z68 / R68C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z71 / R71C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z72 / R72C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z74 / R74C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z78 / R78C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z81 / R81C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z82 / R82C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z83 / R83C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z86 / R86C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z87 / R87C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z89 / R89C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z90 / R90C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z93 / R93C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z95 / R95C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z96 / R96C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z98 / R98C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z99 / R99C26: got a dategdt_biomarker <- dt |>
select(pts_id, ct_dna_surveillance_available,
window_start_date,
surveillance_1_status:surveillance_12_date) |>
filter(ct_dna_surveillance_available) |>
pivot_longer(cols = surveillance_1_status:surveillance_12_date,
names_to = c("visit_number", ".value"),
names_pattern = "surveillance_(.)_(.*)") |>
mutate(biomarker_time = day(days(date - window_start_date))) |>
select(pts_id, biomarker_time, biomarker_status = status) |>
filter(!is.na(biomarker_time))
glimpse(dt_biomarker)Rows: 219
Columns: 3
$ pts_id <chr> "UNM-004", "UNM-004", "UNM-004", "UNM-008", "UNM-008", "UNM-008", "UNM-008", "UNM-008", "UNM-009", "UNM-009", "UNM-009", "UNM-009", "UNM-009", "UNM-014", "UNM-016",…
$ biomarker_time <dbl> 18, 25719, 179, -75, 25647, 154, 236, 322, 46, 25792, 327, 418, 507, 156, 112, 25865, 387, 481, 9, 25746, 265, 28, 477, 19, 25756, 273, 361, 454, 550, 649, 32, 2578…
$ biomarker_status <chr> "NEGATIVE", "POSITIVE", "POSITIVE", "NEGATIVE", "NEGATIVE", "NEGATIVE", "NEGATIVE", NA, "NEGATIVE", "NEGATIVE", "NEGATIVE", "NEGATIVE", "NEGATIVE", "NEGATIVE", "NEG…dt_survival <- dt |>
select(pts_id, ct_dna_surveillance_available,
window_start_date:dfs_date, dfs_event) |> # Added dfs_event here
filter(ct_dna_surveillance_available) |>
mutate(dfs_time = (dfs_date - window_start_date),
dfs_time = day(days(dfs_time)),
dfs_event = as.numeric(dfs_event)) |>
select(pts_id, dfs_time, dfs_event)
glimpse(dt_survival)Rows: 68
Columns: 3
$ pts_id <chr> "UNM-004", "UNM-008", "UNM-009", "UNM-014", "UNM-016", "UNM-018", "UNM-019", "UNM-020", "UNM-021", "UNM-023", "UNM-024", "UNM-025", "UNM-026", "UNM-027", "UNM-028", "UNM-0…
$ dfs_time <dbl> 308, 953, 513, 208, 655, 1058, 1065, 897, 80, 237, 535, 880, 638, 934, 1324, 989, 437, 113, 566, 647, 535, 943, 1069, 108, 591, 150, 305, 283, 126, 918, 192, 411, 366, 102…
$ dfs_event <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, …aux <- dt_survival %>%
filter(dfs_time <= 0)
tab <- left_join(aux, dt) |>
select(pts_id, window_start_date, dfs_time, dfs_date,
surveillance_1_date:surveillance_12_date) |>
mutate(across(.cols = dfs_date:surveillance_12_date,
.fns = ~ as_date(.x))) |>
select(pts_id, window_start_date, dfs_date, dfs_time)Joining with `by = join_by(pts_id, dfs_event)`datatable(tab, filter = "top")
dt_survival <- dt_survival |>
filter(dfs_time > 0)
aux <- dt |>
select(pts_id, ct_dna_surveillance_available,
window_start_date, dfs_date,
surveillance_1_date:surveillance_12_date) |>
mutate(across(.cols = surveillance_1_date:surveillance_12_date,
.fns = ~ .x - window_start_date)) |>
mutate(across(.cols = surveillance_1_date:surveillance_12_date,
.fns = ~ .x < 0)) |>
rowwise() |>
mutate(sum_neg =
sum(c_across(surveillance_1_date:surveillance_12_date),
na.rm = TRUE)) |>
select(pts_id, sum_neg)
tab <- left_join(aux, dt) |>
filter(sum_neg > 0) |>
select(pts_id, sum_neg, window_start_date,
surveillance_1_date:surveillance_12_date) |>
mutate(across(.cols = window_start_date:surveillance_12_date,
.fns = ~ as_date(.x))) Joining with `by = join_by(pts_id)`
G2;H2;Warningh in left_join(aux, dt) :
Detected an unexpected many-to-many relationship between `x` and `y`.
ℹ Row 99 of `x` matches multiple rows in `y`.
ℹ Row 99 of `y` matches multiple rows in `x`.
ℹ If a many-to-many relationship is expected, set `relationship = "many-to-many"` to silence this warning.gdatatable(tab, filter = "top")
aux <- dt |>
select(pts_id, ct_dna_surveillance_available,
window_start_date, dfs_date,
surveillance_1_date:surveillance_12_date) |>
mutate(across(.cols = dfs_date:surveillance_12_date,
.fns = ~ .x - window_start_date)) |>
mutate(across(.cols = surveillance_2_date:surveillance_12_date,
.fns = ~ dfs_date < .x)) |>
rowwise() |>
mutate(n_biomarker_after_event = sum(c_across(surveillance_2_date:
surveillance_12_date),
na.rm = TRUE)) |>
mutate(across(.cols = surveillance_1_date:surveillance_12_date,
.fns = ~ !is.na(.x))) |>
mutate(total_biomarker = sum(c_across(surveillance_2_date:
surveillance_12_date),
na.rm = TRUE)) |>
select(pts_id, n_biomarker_after_event, total_biomarker)
temp <- aux |>
select(-pts_id) |>
group_by(n_biomarker_after_event, total_biomarker) |> # Direct grouping
summarise(freq = n(), .groups = "drop") # Drop groups after summarization
tab <- left_join(aux, dt) |>
select(pts_id, n_biomarker_after_event, total_biomarker,
dfs_date,
surveillance_2_date:surveillance_12_date) |>
mutate(across(.cols = dfs_date:surveillance_12_date,
.fns = ~ as_date(.x))) |>
filter(n_biomarker_after_event > 0)Joining with `by = join_by(pts_id)`
G2;H2;Warningh in left_join(aux, dt) :
Detected an unexpected many-to-many relationship between `x` and `y`.
ℹ Row 99 of `x` matches multiple rows in `y`.
ℹ Row 99 of `y` matches multiple rows in `x`.
ℹ If a many-to-many relationship is expected, set `relationship = "many-to-many"` to silence this warning.gdatatable(tab, filter = "top")
aux <- tmerge(data1 = dt_survival,
data2 = dt_survival,
id = pts_id,
dfs_event = event(dfs_time, dfs_event))
dt_final <- tmerge(data1 = aux,
data2 = dt_biomarker,
id = pts_id,
biomarker_status =
tdc(biomarker_time, biomarker_status))
datatable(dt_final, filter = "top")
# Syntax if there is not time-dependent covariate
# fit <- coxph(Surv(dfs_time, dfs_event) ~ biomarker_status,
# data = dt_final)
# summary(fit)
fit <- coxph(Surv(tstart, tstop, dfs_event) ~ biomarker_status,
data = dt_final)
summary(fit)Call:
coxph(formula = Surv(tstart, tstop, dfs_event) ~ biomarker_status,
data = dt_final)
n= 169, number of events= 16
(66 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
biomarker_statusPOSITIVE 3.2652 26.1856 0.5423 6.021 1.73e-09 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
biomarker_statusPOSITIVE 26.19 0.03819 9.046 75.8
Concordance= 0.737 (se = 0.062 )
Likelihood ratio test= 31.78 on 1 df, p=2e-08
Wald test = 36.25 on 1 df, p=2e-09
Score (logrank) test = 75.43 on 1 df, p=<2e-16cox_fit_summary <- summary(fit)
#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 26.19 (9.05-75.8); p = 0"#Time-dependent analysis for OS in longitudinal time points
rm(list=ls())
setwd("~/Downloads")
dt <- read_xlsx("CLIA HNSCC Peddada Clinical Data_Time dependent.xlsx") |>
clean_names() |>
mutate(across(.cols = c(window_start_date,os_date,
surveillance_1_date:surveillance_12_date),
.fns = ~ as_date(as.Date(.x, format = "%Y-%m-%d"))))G2;H2;Warningh: Expecting numeric in Z5 / R5C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z8 / R8C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z9 / R9C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z15 / R15C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z17 / R17C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z18 / R18C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z19 / R19C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z22 / R22C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z23 / R23C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z25 / R25C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z26 / R26C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z27 / R27C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z28 / R28C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z29 / R29C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z32 / R32C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z34 / R34C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z35 / R35C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z38 / R38C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z41 / R41C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z47 / R47C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z48 / R48C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z49 / R49C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z56 / R56C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z58 / R58C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z60 / R60C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z63 / R63C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z64 / R64C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z65 / R65C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z67 / R67C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z68 / R68C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z71 / R71C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z72 / R72C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z74 / R74C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z78 / R78C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z81 / R81C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z82 / R82C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z83 / R83C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z86 / R86C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z87 / R87C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z89 / R89C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z90 / R90C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z93 / R93C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z95 / R95C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z96 / R96C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z98 / R98C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z99 / R99C26: got a dategdt_biomarker <- dt |>
select(pts_id, ct_dna_surveillance_available,
window_start_date,
surveillance_1_status:surveillance_12_date) |>
filter(ct_dna_surveillance_available) |>
pivot_longer(cols = surveillance_1_status:surveillance_12_date,
names_to = c("visit_number", ".value"),
names_pattern = "surveillance_(.)_(.*)") |>
mutate(biomarker_time = day(days(date - window_start_date))) |>
select(pts_id, biomarker_time, biomarker_status = status) |>
filter(!is.na(biomarker_time))
glimpse(dt_biomarker)Rows: 219
Columns: 3
$ pts_id <chr> "UNM-004", "UNM-004", "UNM-004", "UNM-008", "UNM-008", "UNM-008", "UNM-008", "UNM-008", "UNM-009", "UNM-009", "UNM-009", "UNM-009", "UNM-009", "UNM-014", "UNM-016",…
$ biomarker_time <dbl> 18, 25719, 179, -75, 25647, 154, 236, 322, 46, 25792, 327, 418, 507, 156, 112, 25865, 387, 481, 9, 25746, 265, 28, 477, 19, 25756, 273, 361, 454, 550, 649, 32, 2578…
$ biomarker_status <chr> "NEGATIVE", "POSITIVE", "POSITIVE", "NEGATIVE", "NEGATIVE", "NEGATIVE", "NEGATIVE", NA, "NEGATIVE", "NEGATIVE", "NEGATIVE", "NEGATIVE", "NEGATIVE", "NEGATIVE", "NEG…dt_survival <- dt |>
select(pts_id, ct_dna_surveillance_available,
window_start_date:os_date, os_event) |> # Added os_event here
filter(ct_dna_surveillance_available) |>
mutate(os_time = (os_date - window_start_date),
os_time = day(days(os_time)),
os_event = as.numeric(os_event)) |>
select(pts_id, os_time, os_event)
glimpse(dt_survival)Rows: 68
Columns: 3
$ pts_id <chr> "UNM-004", "UNM-008", "UNM-009", "UNM-014", "UNM-016", "UNM-018", "UNM-019", "UNM-020", "UNM-021", "UNM-023", "UNM-024", "UNM-025", "UNM-026", "UNM-027", "UNM-028", "UNM-02…
$ os_time <dbl> 308, 953, 513, 208, 655, 1058, 1065, 897, 333, 1093, 535, 880, 638, 934, 1324, 989, 437, 897, 1513, 647, 1018, 943, 1069, 336, 591, 150, 305, 283, 126, 918, 192, 411, 366, …
$ os_event <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0…aux <- dt_survival %>%
filter(os_time <= 0)
tab <- left_join(aux, dt) |>
select(pts_id, window_start_date, os_time, os_date,
surveillance_1_date:surveillance_12_date) |>
mutate(across(.cols = os_date:surveillance_12_date,
.fns = ~ as_date(.x))) |>
select(pts_id, window_start_date, os_date, os_time)Joining with `by = join_by(pts_id, os_event)`datatable(tab, filter = "top")
dt_survival <- dt_survival |>
filter(os_time > 0)
aux <- dt |>
select(pts_id, ct_dna_surveillance_available,
window_start_date, os_date,
surveillance_1_date:surveillance_12_date) |>
mutate(across(.cols = surveillance_1_date:surveillance_12_date,
.fns = ~ .x - window_start_date)) |>
mutate(across(.cols = surveillance_1_date:surveillance_12_date,
.fns = ~ .x < 0)) |>
rowwise() |>
mutate(sum_neg =
sum(c_across(surveillance_1_date:surveillance_12_date),
na.rm = TRUE)) |>
select(pts_id, sum_neg)
tab <- left_join(aux, dt) |>
filter(sum_neg > 0) |>
select(pts_id, sum_neg, window_start_date,
surveillance_1_date:surveillance_12_date) |>
mutate(across(.cols = window_start_date:surveillance_12_date,
.fns = ~ as_date(.x))) Joining with `by = join_by(pts_id)`
G2;H2;Warningh in left_join(aux, dt) :
Detected an unexpected many-to-many relationship between `x` and `y`.
ℹ Row 99 of `x` matches multiple rows in `y`.
ℹ Row 99 of `y` matches multiple rows in `x`.
ℹ If a many-to-many relationship is expected, set `relationship = "many-to-many"` to silence this warning.gdatatable(tab, filter = "top")
aux <- dt |>
select(pts_id, ct_dna_surveillance_available,
window_start_date, os_date,
surveillance_1_date:surveillance_12_date) |>
mutate(across(.cols = os_date:surveillance_12_date,
.fns = ~ .x - window_start_date)) |>
mutate(across(.cols = surveillance_2_date:surveillance_12_date,
.fns = ~ os_date < .x)) |>
rowwise() |>
mutate(n_biomarker_after_event = sum(c_across(surveillance_2_date:
surveillance_12_date),
na.rm = TRUE)) |>
mutate(across(.cols = surveillance_1_date:surveillance_12_date,
.fns = ~ !is.na(.x))) |>
mutate(total_biomarker = sum(c_across(surveillance_2_date:
surveillance_12_date),
na.rm = TRUE)) |>
select(pts_id, n_biomarker_after_event, total_biomarker)
temp <- aux |>
select(-pts_id) |>
group_by(n_biomarker_after_event, total_biomarker) |> # Direct grouping
summarise(freq = n(), .groups = "drop") # Drop groups after summarization
tab <- left_join(aux, dt) |>
select(pts_id, n_biomarker_after_event, total_biomarker,
os_date,
surveillance_2_date:surveillance_12_date) |>
mutate(across(.cols = os_date:surveillance_12_date,
.fns = ~ as_date(.x))) |>
filter(n_biomarker_after_event > 0)Joining with `by = join_by(pts_id)`
G2;H2;Warningh in left_join(aux, dt) :
Detected an unexpected many-to-many relationship between `x` and `y`.
ℹ Row 99 of `x` matches multiple rows in `y`.
ℹ Row 99 of `y` matches multiple rows in `x`.
ℹ If a many-to-many relationship is expected, set `relationship = "many-to-many"` to silence this warning.gdatatable(tab, filter = "top")
aux <- tmerge(data1 = dt_survival,
data2 = dt_survival,
id = pts_id,
os_event = event(os_time, os_event))
dt_final <- tmerge(data1 = aux,
data2 = dt_biomarker,
id = pts_id,
biomarker_status =
tdc(biomarker_time, biomarker_status))
datatable(dt_final, filter = "top")
# Syntax if there is not time-dependent covariate
# fit <- coxph(Surv(os_time, os_event) ~ biomarker_status,
# data = dt_final)
# summary(fit)
fit <- coxph(Surv(tstart, tstop, os_event) ~ biomarker_status,
data = dt_final)
summary(fit)Call:
coxph(formula = Surv(tstart, tstop, os_event) ~ biomarker_status,
data = dt_final)
n= 169, number of events= 3
(66 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
biomarker_statusPOSITIVE 1.902 6.698 1.240 1.534 0.125
exp(coef) exp(-coef) lower .95 upper .95
biomarker_statusPOSITIVE 6.698 0.1493 0.5893 76.13
Concordance= 0.637 (se = 0.135 )
Likelihood ratio test= 1.78 on 1 df, p=0.2
Wald test = 2.35 on 1 df, p=0.1
Score (logrank) test = 3.13 on 1 df, p=0.08cox_fit_summary <- summary(fit)
#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 6.7 (0.59-76.13); p = 0.125"#PFS by ctDNA status at surveillance Stage I/II
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$cStage=="I/II",]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)~ctDNA.Surveillance, data = circ_data)Call: survfit(formula = Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event) ~
ctDNA.Surveillance, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.Surveillance=NEGATIVE 31 0 NA NA NA
ctDNA.Surveillance=POSITIVE 6 4 19.6 8.18 NAevent_summary <- circ_data %>%
group_by(ctDNA.Surveillance) %>%
summarise(
Total = n(),
Events = sum(PFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Surveillance, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = TRUE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="PFS - ctDNA at Surveillance Stage I/II", ylab= "Progression-Free Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 12, 24, 36))Call: survfit(formula = surv_object ~ ctDNA.Surveillance, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.Surveillance=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 31 0 1 0 1 1
12 25 0 1 0 NA NA
24 15 0 1 0 NA NA
36 5 0 1 0 NA NA
ctDNA.Surveillance=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 6 0 1.000 0.000 1.000 1.000
12 4 2 0.667 0.192 0.195 0.904
24 3 1 0.500 0.204 0.111 0.804circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxphf(surv_object ~ ctDNA.Surveillance, data=circ_data)
summary(cox_fit)coxphf(formula = surv_object ~ ctDNA.Surveillance, data = circ_data)
Model fitted by Penalized ML
Confidence intervals and p-values by Profile Likelihood
coef se(coef) exp(coef) lower 0.95 upper 0.95 Chisq p
ctDNA.SurveillancePOSITIVE 3.907704 1.668013 49.78452 5.304948 6600.525 13.77645 0.0002059016
Likelihood ratio test=13.77645 on 1 df, p=0.0002059016, n=37
Wald test = 5.488381 on 1 df, p = 0.01914326
Covariance-Matrix:
ctDNA.SurveillancePOSITIVE
ctDNA.SurveillancePOSITIVE 2.782269cox_fit_summary <- summary(cox_fit)coxphf(formula = surv_object ~ ctDNA.Surveillance, data = circ_data)
Model fitted by Penalized ML
Confidence intervals and p-values by Profile Likelihood
coef se(coef) exp(coef) lower 0.95 upper 0.95 Chisq p
ctDNA.SurveillancePOSITIVE 3.907704 1.668013 49.78452 5.304948 6600.525 13.77645 0.0002059016
Likelihood ratio test=13.77645 on 1 df, p=0.0002059016, n=37
Wald test = 5.488381 on 1 df, p = 0.01914326
Covariance-Matrix:
ctDNA.SurveillancePOSITIVE
ctDNA.SurveillancePOSITIVE 2.782269circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PFS.Event <- factor(circ_data$PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression"))
contingency_table <- table(circ_data$ctDNA.Surveillance, circ_data$PFS.Event)
chi_square_test <- chisq.test(contingency_table)G2;H2;Warningh in stats::chisq.test(x, y, ...) :
Chi-squared approximation may be incorrectgprint(chi_square_test)
Pearson's Chi-squared test with Yates' continuity correction
data: contingency_table
X-squared = 16.773, df = 1, p-value = 4.212e-05fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
Fisher's Exact Test for Count Data
data: contingency_table
p-value = 0.0002271
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
5.305224 Inf
sample estimates:
odds ratio
Inf print(contingency_table)
No Progression Progression
Negative 31 0
Positive 2 4table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "ctDNA status at Surveillance Stage I/II",
x = "ctDNA",
y = "Patients (%)",
fill = "Progression",
caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("No Progression" = "blue", "Progression" = "red")) + # define custom colors
theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Progression label size
#PFS by ctDNA status at surveillance Stage III/IV
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$cStage=="III/IV",]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)~ctDNA.Surveillance, data = circ_data)Call: survfit(formula = Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event) ~
ctDNA.Surveillance, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.Surveillance=NEGATIVE 20 3 NA NA NA
ctDNA.Surveillance=POSITIVE 11 9 15.5 11.6 NAevent_summary <- circ_data %>%
group_by(ctDNA.Surveillance) %>%
summarise(
Total = n(),
Events = sum(PFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Surveillance, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="PFS - ctDNA at Surveillance Stage III/IV", ylab= "Progression-Free Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 12, 24, 36))Call: survfit(formula = surv_object ~ ctDNA.Surveillance, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.Surveillance=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 20 0 1.000 0.0000 1.000 1.000
12 15 2 0.900 0.0671 0.656 0.974
24 7 1 0.825 0.0945 0.539 0.942
36 3 0 0.825 0.0945 0.539 0.942
ctDNA.Surveillance=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 11 0 1.000 0.000 1.00000 1.000
12 7 4 0.636 0.145 0.29689 0.845
24 2 4 0.242 0.138 0.04413 0.525
36 1 1 0.121 0.110 0.00739 0.404circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.Surveillance, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.Surveillance, data = circ_data)
n= 31, number of events= 12
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.SurveillancePOSITIVE 1.9270 6.8686 0.6722 2.867 0.00415 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.SurveillancePOSITIVE 6.869 0.1456 1.839 25.65
Concordance= 0.704 (se = 0.074 )
Likelihood ratio test= 9.92 on 1 df, p=0.002
Wald test = 8.22 on 1 df, p=0.004
Score (logrank) test = 10.93 on 1 df, p=9e-04cox_fit_summary <- summary(cox_fit)
#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 6.87 (1.84-25.65); p = 0.004"circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PFS.Event <- factor(circ_data$PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression"))
contingency_table <- table(circ_data$ctDNA.Surveillance, circ_data$PFS.Event)
chi_square_test <- chisq.test(contingency_table)G2;H2;Warningh in stats::chisq.test(x, y, ...) :
Chi-squared approximation may be incorrectgprint(chi_square_test)
Pearson's Chi-squared test with Yates' continuity correction
data: contingency_table
X-squared = 10.687, df = 1, p-value = 0.001079fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
Fisher's Exact Test for Count Data
data: contingency_table
p-value = 0.0004593
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
2.787876 307.695962
sample estimates:
odds ratio
21.73942 print(contingency_table)
No Progression Progression
Negative 17 3
Positive 2 9table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "ctDNA status at Surveillance Stage III/IV",
x = "ctDNA",
y = "Patients (%)",
fill = "Progression",
caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("No Progression" = "blue", "Progression" = "red")) + # define custom colors
theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Progression label size
#PFS by ctDNA status at surveillance p16(+)
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$p16.status=="Positive",]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)~ctDNA.Surveillance, data = circ_data)Call: survfit(formula = Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event) ~
ctDNA.Surveillance, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.Surveillance=NEGATIVE 30 0 NA NA NA
ctDNA.Surveillance=POSITIVE 4 3 19.6 8.18 NAevent_summary <- circ_data %>%
group_by(ctDNA.Surveillance) %>%
summarise(
Total = n(),
Events = sum(PFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Surveillance, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = TRUE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="PFS - ctDNA at Surveillance p16(+)", ylab= "Progression-Free Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 12, 24, 36))Call: survfit(formula = surv_object ~ ctDNA.Surveillance, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.Surveillance=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 30 0 1 0 1 1
12 22 0 1 0 NA NA
24 13 0 1 0 NA NA
36 5 0 1 0 NA NA
ctDNA.Surveillance=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 4 0 1.00 0.000 1.0000 1.000
12 3 1 0.75 0.217 0.1279 0.961
24 2 1 0.50 0.250 0.0578 0.845circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxphf(surv_object ~ ctDNA.Surveillance, data=circ_data)
summary(cox_fit)coxphf(formula = surv_object ~ ctDNA.Surveillance, data = circ_data)
Model fitted by Penalized ML
Confidence intervals and p-values by Profile Likelihood
coef se(coef) exp(coef) lower 0.95 upper 0.95 Chisq p
ctDNA.SurveillancePOSITIVE 3.859906 1.746986 47.46087 4.594352 6384.72 11.46564 0.0007089475
Likelihood ratio test=11.46564 on 1 df, p=0.0007089475, n=34
Wald test = 4.881741 on 1 df, p = 0.02714223
Covariance-Matrix:
ctDNA.SurveillancePOSITIVE
ctDNA.SurveillancePOSITIVE 3.051959cox_fit_summary <- summary(cox_fit)coxphf(formula = surv_object ~ ctDNA.Surveillance, data = circ_data)
Model fitted by Penalized ML
Confidence intervals and p-values by Profile Likelihood
coef se(coef) exp(coef) lower 0.95 upper 0.95 Chisq p
ctDNA.SurveillancePOSITIVE 3.859906 1.746986 47.46087 4.594352 6384.72 11.46564 0.0007089475
Likelihood ratio test=11.46564 on 1 df, p=0.0007089475, n=34
Wald test = 4.881741 on 1 df, p = 0.02714223
Covariance-Matrix:
ctDNA.SurveillancePOSITIVE
ctDNA.SurveillancePOSITIVE 3.051959circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PFS.Event <- factor(circ_data$PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression"))
contingency_table <- table(circ_data$ctDNA.Surveillance, circ_data$PFS.Event)
chi_square_test <- chisq.test(contingency_table)G2;H2;Warningh in stats::chisq.test(x, y, ...) :
Chi-squared approximation may be incorrectgprint(chi_square_test)
Pearson's Chi-squared test with Yates' continuity correction
data: contingency_table
X-squared = 16.235, df = 1, p-value = 5.594e-05fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
Fisher's Exact Test for Count Data
data: contingency_table
p-value = 0.0006684
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
4.703171 Inf
sample estimates:
odds ratio
Inf print(contingency_table)
No Progression Progression
Negative 30 0
Positive 1 3table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "ctDNA status at Surveillance p16(+)",
x = "ctDNA",
y = "Patients (%)",
fill = "Progression",
caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("No Progression" = "blue", "Progression" = "red")) + # define custom colors
theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Progression label size
#PFS by ctDNA status at surveillance p16(-)
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$p16.status=="Negative",]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)~ctDNA.Surveillance, data = circ_data)Call: survfit(formula = Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event) ~
ctDNA.Surveillance, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.Surveillance=NEGATIVE 21 3 NA NA NA
ctDNA.Surveillance=POSITIVE 13 10 15.5 11.3 NAevent_summary <- circ_data %>%
group_by(ctDNA.Surveillance) %>%
summarise(
Total = n(),
Events = sum(PFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Surveillance, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="PFS - ctDNA at Surveillance p16(-)", ylab= "Progression-Free Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 12, 24, 36))Call: survfit(formula = surv_object ~ ctDNA.Surveillance, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.Surveillance=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 21 0 1.000 0.0000 1.000 1.000
12 18 2 0.905 0.0641 0.670 0.975
24 9 1 0.840 0.0861 0.576 0.947
36 3 0 0.840 0.0861 0.576 0.947
ctDNA.Surveillance=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 13 0 1.000 0.000 1.0000 1.000
12 8 5 0.615 0.135 0.3083 0.818
24 3 4 0.288 0.131 0.0785 0.545
36 1 1 0.192 0.117 0.0331 0.450circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.Surveillance, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.Surveillance, data = circ_data)
n= 34, number of events= 13
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.SurveillancePOSITIVE 1.9761 7.2142 0.6622 2.984 0.00285 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.SurveillancePOSITIVE 7.214 0.1386 1.97 26.42
Concordance= 0.717 (se = 0.067 )
Likelihood ratio test= 11.1 on 1 df, p=9e-04
Wald test = 8.9 on 1 df, p=0.003
Score (logrank) test = 12.05 on 1 df, p=5e-04cox_fit_summary <- summary(cox_fit)
#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 7.21 (1.97-26.42); p = 0.003"circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PFS.Event <- factor(circ_data$PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression"))
contingency_table <- table(circ_data$ctDNA.Surveillance, circ_data$PFS.Event)
chi_square_test <- chisq.test(contingency_table)G2;H2;Warningh in stats::chisq.test(x, y, ...) :
Chi-squared approximation may be incorrectgprint(chi_square_test)
Pearson's Chi-squared test with Yates' continuity correction
data: contingency_table
X-squared = 10.819, df = 1, p-value = 0.001005fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
Fisher's Exact Test for Count Data
data: contingency_table
p-value = 0.0006471
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
2.675136 170.607840
sample estimates:
odds ratio
17.58424 print(contingency_table)
No Progression Progression
Negative 18 3
Positive 3 10table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "ctDNA status at Surveillance p16(-)",
x = "ctDNA",
y = "Patients (%)",
fill = "Progression",
caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("No Progression" = "blue", "Progression" = "red")) + # define custom colors
theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Progression label size
#Multivariate cox regression for PFS ctDNA status at surveillance
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"), labels = c("Negative", "Positive"))
circ_data$cStage <- factor(circ_data$cStage, levels = c("I/II", "III/IV"))
circ_data$p16.status <- factor(circ_data$p16.status, levels = c("Negative", "Positive"))
circ_data$Prim.Location <- factor(circ_data$Prim.Location, levels = c("Oropharynx", "Larynx/Hypopharynx", "Oral cavity", "Other (paranasal sinus and nasopharyngeal)"))
surv_object <- Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
cox_fit <- coxph(surv_object ~ ctDNA.Surveillance + cStage + p16.status + Prim.Location, data=circ_data)
ggforest(cox_fit, data = circ_data, main = "Multivariate Regression Model for PFS", refLabel = "Reference Group")
test.ph <- cox.zph(cox_fit)#ctDNA and MTM/mL Dynamics for pts at surveillance window
#Dynamics and MTM/mL plots for patients with ctDNA negative at surveillance
rm(list=ls())
setwd("~/Downloads")
df <- read.csv("CLIA HNSCC ctDNA MTM.csv", stringsAsFactors = FALSE)
df <- df[df$ctDNA.Surveillance=="NEGATIVE",]
df$PFS.Event <- ifelse(df$PFS.Event %in% c("No", "no", "FALSE", "False", "0"), FALSE,
ifelse(df$PFS.Event %in% c("Yes", "yes", "TRUE", "True", "1"), TRUE, NA))
df$PFS.Event <- factor(df$PFS.Event, levels = c(FALSE, TRUE))
df <- df %>%
group_by(PatientName) %>%
filter(n() >= 2) %>% #keep only pts with at least 2 post-surgery time points
ungroup()
num_unique <- length(unique(df$PatientName))
cat("Number of unique patients:", num_unique, "\n")Number of unique patients: 51 df_patient_pfs <- df %>%
group_by(PatientName) %>%
dplyr::summarize(
PFS_True = any(PFS.Event == TRUE, na.rm = TRUE),
PFS_False = all(PFS.Event == FALSE, na.rm = TRUE)
)
num_true <- sum(df_patient_pfs$PFS_True)
num_false <- sum(df_patient_pfs$PFS_False)
cat("Number of unique patients with Event:", num_true, "\n")Number of unique patients with Event: 3 cat("Number of unique patients with No Event:", num_false, "\n")Number of unique patients with No Event: 48 p <- ggplot(df, aes(x = date.diff.months,
y = MTM.mL,
group = PatientName,
color = PFS.Event)) +
geom_line() + # Connect timepoints for each patient
geom_point() + # Add points for each timepoint
# Use a log10 scale for the y-axis with specified breaks
scale_y_log10(breaks = c(0.01, 0.1, 1, 10, 100),
labels = c("0.01","0.1", "1", "10", "100")) +
scale_x_continuous(breaks = seq(0, max(df$date.diff.months, na.rm = TRUE), by = 6)) +
scale_color_manual(values = c("FALSE" = "blue", "TRUE" = "red")) +
labs(
x = "Time Since Surgery or start of definitive treatment (months)",
y = "Mean Tumor Molecules per mL (MTM/mL)",
color = "PFS Event"
) +
theme_minimal()
print(p)
#Dynamics and MTM/mL plots for patients with ctDNA positive at surveillance
rm(list=ls())
setwd("~/Downloads")
df <- read.csv("CLIA HNSCC ctDNA MTM.csv", stringsAsFactors = FALSE)
df <- df[df$ctDNA.Surveillance=="POSITIVE",]
df$PFS.Event <- ifelse(df$PFS.Event %in% c("No", "no", "FALSE", "False", "0"), FALSE,
ifelse(df$PFS.Event %in% c("Yes", "yes", "TRUE", "True", "1"), TRUE, NA))
df$PFS.Event <- factor(df$PFS.Event, levels = c(FALSE, TRUE))
df <- df %>%
group_by(PatientName) %>%
filter(n() >= 2) %>% #keep only pts with at least 2 post-surgery time points
ungroup()
num_unique <- length(unique(df$PatientName))
cat("Number of unique patients:", num_unique, "\n")Number of unique patients: 17 df_patient_pfs <- df %>%
group_by(PatientName) %>%
dplyr::summarize(
PFS_True = any(PFS.Event == TRUE, na.rm = TRUE),
PFS_False = all(PFS.Event == FALSE, na.rm = TRUE)
)
num_true <- sum(df_patient_pfs$PFS_True)
num_false <- sum(df_patient_pfs$PFS_False)
cat("Number of unique patients with Event:", num_true, "\n")Number of unique patients with Event: 13 cat("Number of unique patients with No Event:", num_false, "\n")Number of unique patients with No Event: 4 p <- ggplot(df, aes(x = date.diff.months,
y = MTM.mL,
group = PatientName,
color = PFS.Event)) +
geom_line() + # Connect timepoints for each patient
geom_point() + # Add points for each timepoint
# Use a log10 scale for the y-axis with specified breaks
scale_y_log10(breaks = c(0.01, 0.1, 1, 10, 100),
labels = c("0.01","0.1", "1", "10", "100")) +
scale_x_continuous(breaks = seq(0, max(df$date.diff.months, na.rm = TRUE), by = 6)) +
scale_color_manual(values = c("FALSE" = "blue", "TRUE" = "red")) +
labs(
x = "Time Since Surgery or start of definitive treatment (months)",
y = "Mean Tumor Molecules per mL (MTM/mL)",
color = "PFS Event"
) +
theme_minimal()
print(p)
#ctDNA and MTM/mL Dynamics for pts at surveillance window (excluding baseline & post-progression samples)
#Dynamics and MTM/mL plots for patients with ctDNA negative at surveillance
rm(list=ls())
setwd("~/Downloads")
df <- read.csv("CLIA HNSCC ctDNA MTM.csv", stringsAsFactors = FALSE)
df <- df[!(df$ctDNA.Window %in% c("Baseline", "Post-PD")), ]
df <- df[df$ctDNA.Surveillance=="NEGATIVE",]
df$PFS.Event <- ifelse(df$PFS.Event %in% c("No", "no", "FALSE", "False", "0"), FALSE,
ifelse(df$PFS.Event %in% c("Yes", "yes", "TRUE", "True", "1"), TRUE, NA))
df$PFS.Event <- factor(df$PFS.Event, levels = c(FALSE, TRUE))
df <- df %>%
group_by(PatientName) %>%
filter(n() >= 2) %>% #keep only pts with at least 2 post-surgery time points
ungroup()
num_unique <- length(unique(df$PatientName))
cat("Number of unique patients:", num_unique, "\n")Number of unique patients: 48 p <- ggplot(df, aes(x = date.diff.months,
y = MTM.mL,
group = PatientName,
color = PFS.Event)) +
geom_line() + # Connect timepoints for each patient
geom_point() + # Add points for each timepoint
# Use a log10 scale for the y-axis with specified breaks
scale_y_log10(breaks = c(0.01, 0.1, 1, 10, 100),
labels = c("0.01","0.1", "1", "10", "100")) +
scale_x_continuous(breaks = seq(0, max(df$date.diff.months, na.rm = TRUE), by = 6)) +
scale_color_manual(values = c("FALSE" = "blue", "TRUE" = "red")) +
labs(
x = "Time Since Surgery or start of definitive treatment (months)",
y = "Mean Tumor Molecules per mL (MTM/mL)",
color = "PFS Event"
) +
theme_minimal()
print(p)
#Dynamics and MTM/mL plots for patients with ctDNA positive at surveillance
rm(list=ls())
setwd("~/Downloads")
df <- read.csv("CLIA HNSCC ctDNA MTM.csv", stringsAsFactors = FALSE)
df <- df[!(df$ctDNA.Window %in% c("Baseline", "Post-PD")), ]
df <- df[df$ctDNA.Surveillance=="POSITIVE",]
df$PFS.Event <- ifelse(df$PFS.Event %in% c("No", "no", "FALSE", "False", "0"), FALSE,
ifelse(df$PFS.Event %in% c("Yes", "yes", "TRUE", "True", "1"), TRUE, NA))
df$PFS.Event <- factor(df$PFS.Event, levels = c(FALSE, TRUE))
df <- df %>%
group_by(PatientName) %>%
filter(n() >= 2) %>% #keep only pts with at least 2 post-surgery time points
ungroup()
num_unique <- length(unique(df$PatientName))
cat("Number of unique patients:", num_unique, "\n")Number of unique patients: 16 p <- ggplot(df, aes(x = date.diff.months,
y = MTM.mL,
group = PatientName,
color = PFS.Event)) +
geom_line() + # Connect timepoints for each patient
geom_point() + # Add points for each timepoint
# Use a log10 scale for the y-axis with specified breaks
scale_y_log10(breaks = c(0.01, 0.1, 1, 10, 100),
labels = c("0.01","0.1", "1", "10", "100")) +
scale_x_continuous(breaks = seq(0, max(df$date.diff.months, na.rm = TRUE), by = 6)) +
scale_color_manual(values = c("FALSE" = "blue", "TRUE" = "red")) +
labs(
x = "Time Since Surgery or start of definitive treatment (months)",
y = "Mean Tumor Molecules per mL (MTM/mL)",
color = "PFS Event"
) +
theme_minimal()
print(p)
#PFS by ctDNA status at surveillance for pts with MRD & Surveillance time points available
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.complete=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)~ctDNA.Surveillance, data = circ_data)Call: survfit(formula = Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event) ~
ctDNA.Surveillance, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.Surveillance=NEGATIVE 42 1 NA NA NA
ctDNA.Surveillance=POSITIVE 12 8 15.1 11.3 NAevent_summary <- circ_data %>%
group_by(ctDNA.Surveillance) %>%
summarise(
Total = n(),
Events = sum(PFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Surveillance, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="PFS - ctDNA at Surveillance", ylab= "Progression-Free Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 12, 24, 36))Call: survfit(formula = surv_object ~ ctDNA.Surveillance, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.Surveillance=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 42 0 1.000 0.0000 1.000 1.000
12 34 1 0.976 0.0235 0.843 0.997
24 20 0 0.976 0.0235 0.843 0.997
36 6 0 0.976 0.0235 0.843 0.997
ctDNA.Surveillance=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 12 0 1.000 0.000 1.0000 1.000
12 8 4 0.667 0.136 0.3370 0.860
24 3 4 0.312 0.140 0.0845 0.578
36 1 0 0.312 0.140 0.0845 0.578circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.Surveillance, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.Surveillance, data = circ_data)
n= 54, number of events= 9
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.SurveillancePOSITIVE 3.549 34.774 1.062 3.34 0.000837 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.SurveillancePOSITIVE 34.77 0.02876 4.334 279
Concordance= 0.845 (se = 0.063 )
Likelihood ratio test= 20.83 on 1 df, p=5e-06
Wald test = 11.16 on 1 df, p=8e-04
Score (logrank) test = 28.65 on 1 df, p=9e-08cox_fit_summary <- summary(cox_fit)
#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 34.77 (4.33-279); p = 0.001"circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PFS.Event <- factor(circ_data$PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression"))
contingency_table <- table(circ_data$ctDNA.Surveillance, circ_data$PFS.Event)
chi_square_test <- chisq.test(contingency_table)G2;H2;Warningh in stats::chisq.test(x, y, ...) :
Chi-squared approximation may be incorrectgprint(chi_square_test)
Pearson's Chi-squared test with Yates' continuity correction
data: contingency_table
X-squared = 23.336, df = 1, p-value = 1.361e-06fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
Fisher's Exact Test for Count Data
data: contingency_table
p-value = 3.951e-06
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
6.898669 3612.916842
sample estimates:
odds ratio
69.02378 print(contingency_table)
No Progression Progression
Negative 41 1
Positive 4 8table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "ctDNA status at Surveillance",
x = "ctDNA",
y = "Patients (%)",
fill = "Progression",
caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("No Progression" = "blue", "Progression" = "red")) + # define custom colors
theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Progression label size
#Time-dependent analysis for PFS in longitudinal time points for pts with MRD & Surveillance time points available
rm(list=ls())
setwd("~/Downloads")
dt <- read_xlsx("CLIA HNSCC Peddada Clinical Data_Time dependent.xlsx") |>
clean_names() |>
mutate(across(.cols = c(window_start_date,dfs_date,
surveillance_1_date:surveillance_12_date),
.fns = ~ as_date(as.Date(.x, format = "%Y-%m-%d"))))G2;H2;Warningh: Expecting numeric in Z5 / R5C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z8 / R8C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z9 / R9C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z15 / R15C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z17 / R17C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z18 / R18C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z19 / R19C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z22 / R22C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z23 / R23C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z25 / R25C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z26 / R26C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z27 / R27C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z28 / R28C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z29 / R29C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z32 / R32C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z34 / R34C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z35 / R35C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z38 / R38C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z41 / R41C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z47 / R47C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z48 / R48C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z49 / R49C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z56 / R56C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z58 / R58C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z60 / R60C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z63 / R63C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z64 / R64C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z65 / R65C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z67 / R67C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z68 / R68C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z71 / R71C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z72 / R72C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z74 / R74C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z78 / R78C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z81 / R81C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z82 / R82C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z83 / R83C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z86 / R86C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z87 / R87C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z89 / R89C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z90 / R90C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z93 / R93C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z95 / R95C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z96 / R96C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z98 / R98C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z99 / R99C26: got a dategdt_biomarker <- dt |>
select(pts_id, ct_dna_complete,
window_start_date,
surveillance_1_status:surveillance_12_date) |>
filter(ct_dna_complete) |>
pivot_longer(cols = surveillance_1_status:surveillance_12_date,
names_to = c("visit_number", ".value"),
names_pattern = "surveillance_(.)_(.*)") |>
mutate(biomarker_time = day(days(date - window_start_date))) |>
select(pts_id, biomarker_time, biomarker_status = status) |>
filter(!is.na(biomarker_time))
glimpse(dt_biomarker)Rows: 183
Columns: 3
$ pts_id <chr> "UNM-004", "UNM-004", "UNM-004", "UNM-008", "UNM-008", "UNM-008", "UNM-008", "UNM-008", "UNM-009", "UNM-009", "UNM-009", "UNM-009", "UNM-009", "UNM-014", "UNM-016",…
$ biomarker_time <dbl> 18, 25719, 179, -75, 25647, 154, 236, 322, 46, 25792, 327, 418, 507, 156, 112, 25865, 387, 481, 19, 25756, 273, 361, 454, 550, 649, 32, 25783, 307, 398, 502, 579, 7…
$ biomarker_status <chr> "NEGATIVE", "POSITIVE", "POSITIVE", "NEGATIVE", "NEGATIVE", "NEGATIVE", "NEGATIVE", NA, "NEGATIVE", "NEGATIVE", "NEGATIVE", "NEGATIVE", "NEGATIVE", "NEGATIVE", "NEG…dt_survival <- dt |>
select(pts_id, ct_dna_complete,
window_start_date:dfs_date, dfs_event) |> # Added dfs_event here
filter(ct_dna_complete) |>
mutate(dfs_time = (dfs_date - window_start_date),
dfs_time = day(days(dfs_time)),
dfs_event = as.numeric(dfs_event)) |>
select(pts_id, dfs_time, dfs_event)
glimpse(dt_survival)Rows: 54
Columns: 3
$ pts_id <chr> "UNM-004", "UNM-008", "UNM-009", "UNM-014", "UNM-016", "UNM-019", "UNM-020", "UNM-023", "UNM-024", "UNM-025", "UNM-026", "UNM-027", "UNM-029", "UNM-030", "UNM-031", "UNM-0…
$ dfs_time <dbl> 308, 953, 513, 208, 655, 1065, 897, 237, 535, 880, 638, 934, 989, 437, 113, 647, 535, 943, 1069, 591, 305, 283, 126, 918, 192, 411, 1027, 156, 1048, 270, 565, 338, 141, 87…
$ dfs_event <dbl> 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0aux <- dt_survival %>%
filter(dfs_time <= 0)
tab <- left_join(aux, dt) |>
select(pts_id, window_start_date, dfs_time, dfs_date,
surveillance_1_date:surveillance_12_date) |>
mutate(across(.cols = dfs_date:surveillance_12_date,
.fns = ~ as_date(.x))) |>
select(pts_id, window_start_date, dfs_date, dfs_time)Joining with `by = join_by(pts_id, dfs_event)`datatable(tab, filter = "top")
dt_survival <- dt_survival |>
filter(dfs_time > 0)
aux <- dt |>
select(pts_id, ct_dna_complete,
window_start_date, dfs_date,
surveillance_1_date:surveillance_12_date) |>
mutate(across(.cols = surveillance_1_date:surveillance_12_date,
.fns = ~ .x - window_start_date)) |>
mutate(across(.cols = surveillance_1_date:surveillance_12_date,
.fns = ~ .x < 0)) |>
rowwise() |>
mutate(sum_neg =
sum(c_across(surveillance_1_date:surveillance_12_date),
na.rm = TRUE)) |>
select(pts_id, sum_neg)
tab <- left_join(aux, dt) |>
filter(sum_neg > 0) |>
select(pts_id, sum_neg, window_start_date,
surveillance_1_date:surveillance_12_date) |>
mutate(across(.cols = window_start_date:surveillance_12_date,
.fns = ~ as_date(.x))) Joining with `by = join_by(pts_id)`
G2;H2;Warningh in left_join(aux, dt) :
Detected an unexpected many-to-many relationship between `x` and `y`.
ℹ Row 99 of `x` matches multiple rows in `y`.
ℹ Row 99 of `y` matches multiple rows in `x`.
ℹ If a many-to-many relationship is expected, set `relationship = "many-to-many"` to silence this warning.gdatatable(tab, filter = "top")
aux <- dt |>
select(pts_id, ct_dna_complete,
window_start_date, dfs_date,
surveillance_1_date:surveillance_12_date) |>
mutate(across(.cols = dfs_date:surveillance_12_date,
.fns = ~ .x - window_start_date)) |>
mutate(across(.cols = surveillance_2_date:surveillance_12_date,
.fns = ~ dfs_date < .x)) |>
rowwise() |>
mutate(n_biomarker_after_event = sum(c_across(surveillance_2_date:
surveillance_12_date),
na.rm = TRUE)) |>
mutate(across(.cols = surveillance_1_date:surveillance_12_date,
.fns = ~ !is.na(.x))) |>
mutate(total_biomarker = sum(c_across(surveillance_2_date:
surveillance_12_date),
na.rm = TRUE)) |>
select(pts_id, n_biomarker_after_event, total_biomarker)
temp <- aux |>
select(-pts_id) |>
group_by(n_biomarker_after_event, total_biomarker) |> # Direct grouping
summarise(freq = n(), .groups = "drop") # Drop groups after summarization
tab <- left_join(aux, dt) |>
select(pts_id, n_biomarker_after_event, total_biomarker,
dfs_date,
surveillance_2_date:surveillance_12_date) |>
mutate(across(.cols = dfs_date:surveillance_12_date,
.fns = ~ as_date(.x))) |>
filter(n_biomarker_after_event > 0)Joining with `by = join_by(pts_id)`
G2;H2;Warningh in left_join(aux, dt) :
Detected an unexpected many-to-many relationship between `x` and `y`.
ℹ Row 99 of `x` matches multiple rows in `y`.
ℹ Row 99 of `y` matches multiple rows in `x`.
ℹ If a many-to-many relationship is expected, set `relationship = "many-to-many"` to silence this warning.gdatatable(tab, filter = "top")
aux <- tmerge(data1 = dt_survival,
data2 = dt_survival,
id = pts_id,
dfs_event = event(dfs_time, dfs_event))
dt_final <- tmerge(data1 = aux,
data2 = dt_biomarker,
id = pts_id,
biomarker_status =
tdc(biomarker_time, biomarker_status))
datatable(dt_final, filter = "top")
# Syntax if there is not time-dependent covariate
# fit <- coxph(Surv(dfs_time, dfs_event) ~ biomarker_status,
# data = dt_final)
# summary(fit)
fit <- coxph(Surv(tstart, tstop, dfs_event) ~ biomarker_status,
data = dt_final)
summary(fit)Call:
coxph(formula = Surv(tstart, tstop, dfs_event) ~ biomarker_status,
data = dt_final)
n= 141, number of events= 9
(52 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
biomarker_statusPOSITIVE 3.6249 37.5215 0.7191 5.041 4.63e-07 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
biomarker_statusPOSITIVE 37.52 0.02665 9.166 153.6
Concordance= 0.795 (se = 0.082 )
Likelihood ratio test= 24.07 on 1 df, p=9e-07
Wald test = 25.41 on 1 df, p=5e-07
Score (logrank) test = 63.8 on 1 df, p=1e-15cox_fit_summary <- summary(fit)
#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 37.52 (9.17-153.6); p = 0"#Median numbers of time points and lead time in the longitudinal setting for pts with MRD & Surveillance time points available
# Load the dataset
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.complete=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_datadf <- as.data.frame(circ_data)
median_Nsurvtps <- median(circ_datadf$Nsurvtps, na.rm = TRUE)
min_Nsurvtps <- min(circ_datadf$Nsurvtps, na.rm = TRUE)
max_Nsurvtps <- max(circ_datadf$Nsurvtps, na.rm = TRUE)
cat(sprintf("Median # of surveillance time points: %d (%d-%d)\n",
median_Nsurvtps, min_Nsurvtps, max_Nsurvtps))Median # of surveillance time points: 4 (1-13)circ_datadf$LeadTime_Months <- circ_datadf$LeadTime / 30.437
median_LeadTime <- median(circ_datadf$LeadTime_Months, na.rm = TRUE)
min_LeadTime <- min(circ_datadf$LeadTime_Months, na.rm = TRUE)
max_LeadTime <- max(circ_datadf$LeadTime_Months, na.rm = TRUE)
cat(sprintf("Longitudinally, ctDNA positivity preceded progression by a median of %.2f mo (%.2f–%.2f)\n",
median_LeadTime, min_LeadTime, max_LeadTime))Longitudinally, ctDNA positivity preceded progression by a median of 4.25 mo (0.62–13.96)#PFS by ctDNA status anytime
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.anytime!="",]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)~ctDNA.anytime, data = circ_data)Call: survfit(formula = Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event) ~
ctDNA.anytime, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.anytime=NEGATIVE 55 3 NA NA NA
ctDNA.anytime=POSITIVE 30 21 14.7 11.3 24.8event_summary <- circ_data %>%
group_by(ctDNA.anytime) %>%
summarise(
Total = n(),
Events = sum(PFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.anytime, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="PFS - ctDNA anytime", ylab= "Progression-Free Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 12, 24, 36))Call: survfit(formula = surv_object ~ ctDNA.anytime, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.anytime=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 55 0 1.000 0.0000 1.000 1.000
12 44 2 0.964 0.0252 0.862 0.991
24 26 1 0.935 0.0371 0.807 0.979
36 9 0 0.935 0.0371 0.807 0.979
ctDNA.anytime=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 30 0 1.000 0.0000 1.0000 1.000
12 17 12 0.600 0.0894 0.4045 0.750
24 7 7 0.333 0.0909 0.1670 0.508
36 3 2 0.238 0.0863 0.0947 0.417circ_data$ctDNA.anytime <- factor(circ_data$ctDNA.anytime, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.anytime, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.anytime, data = circ_data)
n= 85, number of events= 24
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.anytimePOSITIVE 2.9021 18.2128 0.6187 4.69 2.73e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.anytimePOSITIVE 18.21 0.05491 5.416 61.24
Concordance= 0.794 (se = 0.038 )
Likelihood ratio test= 37.32 on 1 df, p=1e-09
Wald test = 22 on 1 df, p=3e-06
Score (logrank) test = 41.85 on 1 df, p=1e-10cox_fit_summary <- summary(cox_fit)
#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 18.21 (5.42-61.24); p = 0"circ_data$ctDNA.anytime <- factor(circ_data$ctDNA.anytime, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PFS.Event <- factor(circ_data$PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression"))
contingency_table <- table(circ_data$ctDNA.anytime, circ_data$PFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
Pearson's Chi-squared test with Yates' continuity correction
data: contingency_table
X-squared = 36.789, df = 1, p-value = 1.316e-09fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
Fisher's Exact Test for Count Data
data: contingency_table
p-value = 4.294e-10
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
8.850524 237.891004
sample estimates:
odds ratio
37.70824 print(contingency_table)
No Progression Progression
Negative 52 3
Positive 9 21table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "ctDNA status anytime",
x = "ctDNA",
y = "Patients (%)",
fill = "Progression",
caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("No Progression" = "blue", "Progression" = "red")) + # define custom colors
theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Progression label size
#OS by ctDNA status anytime
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.anytime!="",]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$OS.months, event = circ_data$OS.Event)~ctDNA.anytime, data = circ_data)Call: survfit(formula = Surv(time = circ_data$OS.months, event = circ_data$OS.Event) ~
ctDNA.anytime, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.anytime=NEGATIVE 55 1 NA NA NA
ctDNA.anytime=POSITIVE 30 7 NA NA NAevent_summary <- circ_data %>%
group_by(ctDNA.anytime) %>%
summarise(
Total = n(),
Events = sum(OS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$OS.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.anytime, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="OS - ctDNA anytime", ylab= "Overall Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(12, 24, 36))Call: survfit(formula = surv_object ~ ctDNA.anytime, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.anytime=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 45 1 0.982 0.018 0.878 0.997
24 26 0 0.982 0.018 0.878 0.997
36 9 0 0.982 0.018 0.878 0.997
ctDNA.anytime=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 24 4 0.867 0.0621 0.683 0.948
24 13 3 0.735 0.0885 0.516 0.867
36 8 0 0.735 0.0885 0.516 0.867circ_data$ctDNA.anytime <- factor(circ_data$ctDNA.anytime, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.anytime, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.anytime, data = circ_data)
n= 85, number of events= 8
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.anytimePOSITIVE 2.621 13.743 1.069 2.451 0.0142 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.anytimePOSITIVE 13.74 0.07276 1.691 111.7
Concordance= 0.766 (se = 0.066 )
Likelihood ratio test= 10 on 1 df, p=0.002
Wald test = 6.01 on 1 df, p=0.01
Score (logrank) test = 10.33 on 1 df, p=0.001cox_fit_summary <- summary(cox_fit)
#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 13.74 (1.69-111.72); p = 0.014"circ_data$ctDNA.anytime <- factor(circ_data$ctDNA.anytime, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$OS.Event <- factor(circ_data$OS.Event, levels = c("FALSE", "TRUE"), labels = c("Alive", "Deceased"))
contingency_table <- table(circ_data$ctDNA.anytime, circ_data$OS.Event)
chi_square_test <- chisq.test(contingency_table)G2;H2;Warningh in stats::chisq.test(x, y, ...) :
Chi-squared approximation may be incorrectgprint(chi_square_test)
Pearson's Chi-squared test with Yates' continuity correction
data: contingency_table
X-squared = 8.1668, df = 1, p-value = 0.004266fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
Fisher's Exact Test for Count Data
data: contingency_table
p-value = 0.002448
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
1.874902 750.814710
sample estimates:
odds ratio
15.89819 print(contingency_table)
Alive Deceased
Negative 54 1
Positive 23 7table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "ctDNA status anytime",
x = "ctDNA",
y = "Patients (%)",
fill = "Living Status",
caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("Alive" = "blue", "Deceased" = "red")) + # define custom colors
theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Progression label size
#Median numbers of time points and lead time anytime post-surery or definitive treatment
# Load the dataset
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.anytime!="",]
circ_datadf <- as.data.frame(circ_data)
median_Nsurvtps <- median(circ_datadf$Ntotaltps, na.rm = TRUE)
min_Nsurvtps <- min(circ_datadf$Ntotaltps, na.rm = TRUE)
max_Nsurvtps <- max(circ_datadf$Ntotaltps, na.rm = TRUE)
cat(sprintf("Median # of time points anytimes: %d (%d-%d)\n",
median_Nsurvtps, min_Nsurvtps, max_Nsurvtps))Median # of time points anytimes: 4 (1-16)circ_datadf$LeadTime_Months <- circ_datadf$Anytime.LeadTime / 30.437
median_LeadTime <- median(circ_datadf$LeadTime_Months, na.rm = TRUE)
min_LeadTime <- min(circ_datadf$LeadTime_Months, na.rm = TRUE)
max_LeadTime <- max(circ_datadf$LeadTime_Months, na.rm = TRUE)
cat(sprintf("Anytime post-surgery or start of definitive treatment, ctDNA positivity preceded progression by a median of %.2f mo (%.2f–%.2f)\n",
median_LeadTime, min_LeadTime, max_LeadTime))Anytime post-surgery or start of definitive treatment, ctDNA positivity preceded progression by a median of 3.25 mo (0.00–21.49)---
title: "CLIA HNSCC UNM Clinical Analysis 02272025"
output: html_notebook
---

library(swimplot)
library(grid)
library(gtable)
library(readr) 
library(mosaic)
library(dplyr) 
library(survival) 
library(survminer) 
library(ggplot2)
library(scales)
library(coxphf)
library(ggthemes)
library(tidyverse)
library(gtsummary)
library(flextable)
library(reshape2)
library(parameters)
library(car)
library(ComplexHeatmap)
library(tidyverse)
library(readxl)
library(janitor)
library(DT)
library(pROC)
library(rms)

#ctDNA Detection Rates by Window and Stages
```{r}
#ctDNA at Baseline
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_data$ctDNA.Base <- factor(circ_data$ctDNA.Base, levels=c("NEGATIVE","POSITIVE"))
circ_data <- subset(circ_data, ctDNA.Base %in% c("NEGATIVE", "POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("I/II","III/IVA/IVB","IVC"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.Base == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.Base, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
  Stage = total_counts_by_stage$Group.1,
  Total_Count = total_counts_by_stage$x,
  Positive_Count = positive_counts_by_stage$x,
  Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100  # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.Base == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100  # Convert to percentage
overall_row <- data.frame(
  Stage = "Overall",
  Total_Count = overall_total_count,
  Positive_Count = overall_positive_count,
  Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)

#ctDNA at MRD
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("I/II","III/IVA/IVB","IVC"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.MRD == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.MRD, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
  Stage = total_counts_by_stage$Group.1,
  Total_Count = total_counts_by_stage$x,
  Positive_Count = positive_counts_by_stage$x,
  Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100  # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.MRD == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100  # Convert to percentage
overall_row <- data.frame(
  Stage = "Overall",
  Total_Count = overall_total_count,
  Positive_Count = overall_positive_count,
  Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)

#ctDNA at Surveillance
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("I/II","III/IVA/IVB","IVC"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.Surveillance == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.Surveillance, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
  Stage = total_counts_by_stage$Group.1,
  Total_Count = total_counts_by_stage$x,
  Positive_Count = positive_counts_by_stage$x,
  Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100  # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.Surveillance == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100  # Convert to percentage
overall_row <- data.frame(
  Stage = "Overall",
  Total_Count = overall_total_count,
  Positive_Count = overall_positive_count,
  Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)
```



#Demographics Table
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]

circ_data_subset <- circ_data %>%
  select(
    Sex,
    Age,
    Tobacco.History,
    Prim.Location,
    cT,
    cN,
    cM,
    Histology,
    Stage,
    p16.status,
    Treatment.Group,
    PFS.Event,
    OS.Event,
    OS.months) %>%
  mutate(
    Sex = factor(Sex),
    Age = as.numeric(Age),
    Tobacco.History = factor(Tobacco.History),
    Prim.Location = factor(Prim.Location),
    cT = factor(cT),
    cN = factor(cN),
    cM = factor(cM),
    Histology = factor(Histology),
    Stage = factor(Stage),
    p16.status = factor(p16.status),
    Treatment.Group = factor(Treatment.Group),
    PFS.Event = factor(PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression")),
    OS.Event = factor(OS.Event, levels = c("FALSE", "TRUE"), labels = c("Alive", "Deceased")),
    OS.months = as.numeric(OS.months)) 
table1 <- circ_data_subset %>%
  tbl_summary(
    statistic = list(
      all_continuous() ~ "{median} ({min} - {max})",
      all_categorical() ~ "{n} ({p}%)")) %>%
  bold_labels()
table1
fit1 <- as_flex_table(
  table1,
  include = everything(),
  return_calls = FALSE
)
fit1
save_as_docx(fit1, path= "~/Downloads/1. CLIA HNSCC UNM Demographics Table.docx")
```



#Demographics Table by ctDNA at baseline
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]

circ_data_subset1 <- circ_data %>%
  select(
    Sex,
    Age,
    Tobacco.History,
    Prim.Location,
    cT,
    cN,
    cM,
    Histology,
    Stage,
    p16.status,
    Treatment.Group,
    PFS.Event,
    OS.Event,
    OS.months) %>%
  mutate(
    Sex = factor(Sex),
    Age = as.numeric(Age),
    Tobacco.History = factor(Tobacco.History),
    Prim.Location = factor(Prim.Location),
    cT = factor(cT),
    cN = factor(cN),
    cM = factor(cM),
    Histology = factor(Histology),
    Stage = factor(Stage),
    p16.status = factor(p16.status),
    Treatment.Group = factor(Treatment.Group),
    PFS.Event = factor(PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression")),
    OS.Event = factor(OS.Event, levels = c("FALSE", "TRUE"), labels = c("Alive", "Deceased")),
    OS.months = as.numeric(OS.months)) 

circ_data1 <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]

circ_data_subset2 <- circ_data1 %>%
  select(
    Sex,
    Age,
    Tobacco.History,
    Prim.Location,
    cT,
    cN,
    cM,
    Histology,
    Stage,
    p16.status,
    Treatment.Group,
    PFS.Event,
    OS.Event,
    OS.months,
    ctDNA.Base) %>%
  mutate(
    Sex = factor(Sex),
    Age = as.numeric(Age),
    Tobacco.History = factor(Tobacco.History),
    Prim.Location = factor(Prim.Location),
    cT = factor(cT),
    cN = factor(cN),
    cM = factor(cM),
    Histology = factor(Histology),
    Stage = factor(Stage),
    p16.status = factor(p16.status),
    Treatment.Group = factor(Treatment.Group),
    PFS.Event = factor(PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression")),
    OS.Event = factor(OS.Event, levels = c("FALSE", "TRUE"), labels = c("Alive", "Deceased")),
    OS.months = as.numeric(OS.months),
    ctDNA.Base = factor(ctDNA.Base, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive")))
Overall <- circ_data_subset1 %>%
  tbl_summary(
    statistic = list(
      all_continuous() ~ "{median} ({min} - {max})",
      all_categorical() ~ "{n} ({p}%)")) %>%
  bold_labels()
Overall

ByctDNA_MRD <- circ_data_subset2 %>%
  tbl_summary(
    by = ctDNA.Base, # add this line to subgroup by ctDNA.Base
    statistic = list(
      all_continuous() ~ "{median} ({min} - {max})",
      all_categorical() ~ "{n} ({p}%)")) %>%
  add_p() %>%
  bold_labels()
ByctDNA_MRD

merged_table <- tbl_merge(tbls=list(Overall, ByctDNA_MRD))
merged_table

fit1 <- as_flex_table(
  merged_table,
  include = everything(),
  return_calls = FALSE
)
fit1
save_as_docx(fit1, path = "~/Downloads/1b. CLIA HNSCC UNM Demographics Table by ctDNA.docx")
```

#Overview plot by Stage
```{r}
setwd("~/Downloads") 
clinstage <- read.csv("CLIA HNSCC UNM_OP.csv")
clinstage_df <- as.data.frame(clinstage)

# Creating the basic swimmer plot
oplot <- swimmer_plot(df=clinstage_df,
                      id='PatientName',
                      end='fu.diff.months',
                      fill='gray',
                      width=.01,
                      base_size = 14,
                      stratify= c('Stage'))

# Adding themes and scales
oplot <- oplot + theme(panel.border = element_blank())
oplot <- oplot + scale_y_continuous(breaks = seq(0, 72, by = 3))
oplot <- oplot + labs(x ="Patients", y="Months from Diagnosis")

# Adding swimmer points
oplot_ev1 <- oplot + swimmer_points(df_points=clinstage_df,
                                    id='PatientName',
                                    time='date.diff.months',
                                    name_shape ='Event_type',
                                    name_col = 'Event',
                                    size=3.5,fill='black')
# Optionally uncomment and use col='darkgreen' if needed

# Adding shape manual scale
oplot_ev1.1 <- oplot_ev1 + ggplot2::scale_shape_manual(name="Event_type",
                                                       values=c(1,16,6,18,18,4),
                                                       breaks=c('ctDNA_neg','ctDNA_pos', 'Imaging','Surgery','Biopsy', 'Death'))

# Display the plot
oplot_ev1.1
oplot_ev2 <- oplot_ev1.1 + swimmer_lines(df_lines=clinstage_df,
                                         id='PatientName',
                                         start='Tx_start.months',
                                         end='Tx_end.months',
                                         name_col='Tx_type',
                                         size=3.5,
                                         name_alpha = 1.0)
oplot_ev2 <- oplot_ev2 + guides(linetype = guide_legend(override.aes = list(size = 5, color = "black")))
oplot_ev2
oplot_ev2.2 <- oplot_ev2 + ggplot2::scale_color_manual(name="Event",values=c( "grey", "orange", "black", "black", "green", "red", "purple", "blue"))
oplot_ev2.2
```

#PFS in Complete Cohort (N=97)
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]

survfit(Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)~ctDNA.available, data = circ_data)
surv_object <-Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.available, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue"), title="PFS - Complete Cohort (n=97)", ylab= "Progression-Free Survival", xlab="Months from Start of definitive Treatment", legend.labs=c("Complete cohort"), legend.title="")
summary(KM_curve, times= c(12, 24, 36))
```

#OS in Complete Cohort (N=97)
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]

survfit(Surv(time = circ_data$OS.months, event = circ_data$OS.Event)~ctDNA.available, data = circ_data)
surv_object <-Surv(time = circ_data$OS.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.available, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue"), title="OS - Complete Cohort (n=97)", ylab= "Overall Survival", xlab="Months from Start of definitive Treatment", legend.labs=c("Complete cohort"), legend.title="")
summary(KM_curve, times= c(12, 24, 36))
```

#Association of Baseline ctDNA MTM levels with clinicopathological factors
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_datadf <- as.data.frame(circ_data)

tally(~cStage, data=circ_data, margins = TRUE)
circ_data$cStage <- factor(circ_data$cStage, levels = c("I/II","III/IV"), labels = c("I/II (n=34)","III/IV (n=29)"))
boxplot(ctDNA.Base.MTM~cStage, data=circ_data, main="ctDNA pre-treatment MTM - Stage", xlab="Stage", ylab="MTM/mL", col="white",border="black", ylim = c(0, 200))
median_ctDNA.Stage <- circ_data %>%
  group_by(cStage) %>%
  summarise(median_ctDNA_Base_MTM = median(ctDNA.Base.MTM, na.rm = TRUE))
print(median_ctDNA.Stage)
m1<-wilcox.test(ctDNA.Base.MTM ~ cStage, data=circ_data, na.rm=TRUE, exact=FALSE, conf.int=TRUE)
print(m1)

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_datadf <- as.data.frame(circ_data)

tally(~cT.status, data=circ_data, margins = TRUE)
circ_data$cT.status <- factor(circ_data$cT.status, levels = c("T0-T2","T3-T4"), labels = c("T0-T2 (n=28)","T3-T4 (n=34)"))
boxplot(ctDNA.Base.MTM~cT.status, data=circ_data, main="ctDNA pre-treatment MTM - T stage", xlab="T stage", ylab="MTM/mL", col="white",border="black", ylim = c(0, 200))
median_ctDNA.cT <- circ_data %>%
  group_by(cT.status) %>%
  summarise(median_ctDNA_Base_MTM = median(ctDNA.Base.MTM, na.rm = TRUE))
print(median_ctDNA.cT)
m2<-wilcox.test(ctDNA.Base.MTM ~ cT.status, data=circ_data, na.rm=TRUE, exact=FALSE, conf.int=TRUE)
print(m2)

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_datadf <- as.data.frame(circ_data)

tally(~cT, data=circ_data, margins = TRUE)
circ_data$cT <- factor(circ_data$cT, levels = c("T0","T1","T2","T3","T4"))
boxplot(ctDNA.Base.MTM~cT, data=circ_data, main="ctDNA pre-treatment MTM - cT status", xlab="cT status", ylab="MTM/mL", col="white",border="black", ylim = c(0, 200))
median_ctDNA.cT <- circ_data %>%
  group_by(cT) %>%
  summarise(median_ctDNA_Base_MTM = median(ctDNA.Base.MTM, na.rm = TRUE))
print(median_ctDNA.cT)
pairwise_wilcox <- pairwise.wilcox.test(circ_data$ctDNA.Base.MTM, circ_data$cT, 
                                        p.adjust.method = "none", 
                                        exact = FALSE)
print(pairwise_wilcox)

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available == "TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Base != "",]
circ_data$cT <- factor(circ_data$cT, levels = c("T0", "T1", "T2", "T3", "T4"))
circ_data$ctDNA.Base.MTM <- as.numeric(circ_data$ctDNA.Base.MTM)
cT_levels <- levels(circ_data$cT)
p_value_matrix <- matrix(NA, nrow = length(cT_levels), ncol = length(cT_levels))
rownames(p_value_matrix) <- cT_levels
colnames(p_value_matrix) <- cT_levels

for (i in 1:length(cT_levels)) {
  for (j in i:length(cT_levels)) {
    if (i != j) {
      # Extract data for both groups
      data1 <- circ_data %>% filter(cT == cT_levels[i]) %>% pull(ctDNA.Base.MTM)
      data2 <- circ_data %>% filter(cT == cT_levels[j]) %>% pull(ctDNA.Base.MTM)
      
      # Perform Wilcoxon test and store p-value
      test_result <- wilcox.test(data1, data2, exact = FALSE)
      p_value_matrix[i, j] <- test_result$p.value
      p_value_matrix[j, i] <- test_result$p.value  # Make symmetric
    } else {
      p_value_matrix[i, j] <- 1  # Self-comparison = 1
    }
  }
}

p_value_matrix[is.na(p_value_matrix)] <- 1.00
p_value_data <- melt(p_value_matrix)
colnames(p_value_data) <- c("cT1", "cT2", "p_value")
p_value_data <- p_value_data %>%
  mutate(
    significance = case_when(
      p_value < 0.001 ~ "***",
      p_value < 0.01 ~ "**",
      p_value < 0.05 ~ "*",
      TRUE ~ ""
    )
  )

ggplot(p_value_data, aes(x = cT1, y = cT2, fill = p_value)) +
  geom_tile(color = "white", size = 0.8) +  # Thicker grid lines for separation
  geom_text(aes(label = significance), color = "black", size = 6, fontface = "bold") +  # Significance markers
  scale_fill_gradient2(low = "blue", mid = "white", high = "red", midpoint = 0.05) +  # Gradient colors
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 12, face = "bold"),
        axis.text.y = element_text(size = 12, face = "bold"),
        panel.grid = element_blank()) +
  labs(title = "Pairwise Wilcoxon-Test P-Values (ctDNA.Base.MTM by cT)",
       x = "cT Status", y = "cT Status", fill = "P-Value")

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_datadf <- as.data.frame(circ_data)

tally(~cN.status, data=circ_data, margins = TRUE)
circ_data$cN.status <- factor(circ_data$cN.status, levels = c("N0","N1-N3"), labels = c("N0 (n=12)","N1-N3 (n=50)"))
boxplot(ctDNA.Base.MTM~cN.status, data=circ_data, main="ctDNA pre-treatment MTM - cN status", xlab="cN status", ylab="MTM/mL", col="white",border="black", ylim = c(0, 200))
median_ctDNA.cN <- circ_data %>%
  group_by(cN.status) %>%
  summarise(median_ctDNA_Base_MTM = median(ctDNA.Base.MTM, na.rm = TRUE))
print(median_ctDNA.cN)
m3<-wilcox.test(ctDNA.Base.MTM ~ cN.status, data=circ_data, na.rm=TRUE, exact=FALSE, conf.int=TRUE)
print(m3)

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_datadf <- as.data.frame(circ_data)

tally(~cN, data=circ_data, margins = TRUE)
circ_data$cN <- factor(circ_data$cN, levels = c("N0","N1","N2","N3"))
boxplot(ctDNA.Base.MTM~cN, data=circ_data, main="ctDNA pre-treatment MTM - N Stage", xlab="N Stage", ylab="MTM/mL", col="white",border="black", ylim = c(0, 500))
median_ctDNA.cN <- circ_data %>%
  group_by(cN) %>%
  summarise(median_ctDNA_Base_MTM = median(ctDNA.Base.MTM, na.rm = TRUE))
print(median_ctDNA.cN)
pairwise_wilcox <- pairwise.wilcox.test(circ_data$ctDNA.Base.MTM, circ_data$cN, 
                                        p.adjust.method = "none", 
                                        exact = FALSE)
print(pairwise_wilcox)

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available == "TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Base != "",]
circ_data$cN <- factor(circ_data$cN, levels = c("N0","N1","N2","N3"))
circ_data$ctDNA.Base.MTM <- as.numeric(circ_data$ctDNA.Base.MTM)
cN_levels <- levels(circ_data$cN)
p_value_matrix <- matrix(NA, nrow = length(cN_levels), ncol = length(cN_levels))
rownames(p_value_matrix) <- cN_levels
colnames(p_value_matrix) <- cN_levels

for (i in 1:length(cN_levels)) {
  for (j in i:length(cN_levels)) {
    if (i != j) {
      # Extract data for both groups
      data1 <- circ_data %>% filter(cN == cN_levels[i]) %>% pull(ctDNA.Base.MTM)
      data2 <- circ_data %>% filter(cN == cN_levels[j]) %>% pull(ctDNA.Base.MTM)
      
      # Perform Wilcoxon test and store p-value
      test_result <- wilcox.test(data1, data2, exact = FALSE)
      p_value_matrix[i, j] <- test_result$p.value
      p_value_matrix[j, i] <- test_result$p.value  # Make symmetric
    } else {
      p_value_matrix[i, j] <- 1  # Self-comparison = 1
    }
  }
}

p_value_matrix[is.na(p_value_matrix)] <- 1.00
p_value_data <- melt(p_value_matrix)
colnames(p_value_data) <- c("cN1", "cN2", "p_value")
p_value_data <- p_value_data %>%
  mutate(
    significance = case_when(
      p_value < 0.001 ~ "***",
      p_value < 0.01 ~ "**",
      p_value < 0.05 ~ "*",
      TRUE ~ ""
    )
  )

ggplot(p_value_data, aes(x = cN1, y = cN2, fill = p_value)) +
  geom_tile(color = "white", size = 0.8) +  # Thicker grid lines for separation
  geom_text(aes(label = significance), color = "black", size = 6, fontface = "bold") +  # Significance markers
  scale_fill_gradient2(low = "blue", mid = "white", high = "red", midpoint = 0.05) +  # Gradient colors
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 12, face = "bold"),
        axis.text.y = element_text(size = 12, face = "bold"),
        panel.grid = element_blank()) +
  labs(title = "Pairwise Wilcoxon-Test P-Values (ctDNA.Base.MTM by cN)",
       x = "Status", y = "cN Status", fill = "P-Value")

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available == "TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Base != "",]
circ_data$cT <- factor(circ_data$cT, levels = c("T0", "T1", "T2", "T3", "T4"))
circ_data$cN <- factor(circ_data$cN, levels = c("N0", "N1", "N2", "N3"))
circ_data$ctDNA.Base.MTM <- as.numeric(circ_data$ctDNA.Base.MTM)

median_ctDNA <- circ_data %>%
  group_by(cT, cN) %>%
  summarise(median_ctDNA_Base_MTM = median(ctDNA.Base.MTM, na.rm = TRUE)) %>%
  ungroup()

p_value_matrix <- dcast(median_ctDNA, cT ~ cN, value.var = "median_ctDNA_Base_MTM")
p_value_data <- melt(p_value_matrix, id.vars = "cT", variable.name = "cN", value.name = "median_value")
p_value_data$missing <- ifelse(is.na(p_value_data$median_value), "Missing", "Present")
p_value_data$median_value[is.na(p_value_data$median_value)] <- 0

ggplot(p_value_data, aes(x = cN, y = cT, fill = median_value)) +
  geom_tile(color = "black", size = 0.5) +  # Black gridlines for separation
  geom_text(aes(label = round(median_value, 2)), color = "black", size = 5) +  # Display median values
  scale_fill_gradient(low = "white", high = "blue") +  # Color gradient similar to the reference image
  theme_minimal() +
  theme(axis.text.x = element_text(size = 12, face = "bold"),
        axis.text.y = element_text(size = 12, face = "bold"),
        panel.grid = element_blank()) +
  labs(title = "Median ctDNA.Base.MTM by cT and cN",
       x = "cN Status", y = "cT Status", fill = "Median MTM") +
  geom_tile(data = subset(p_value_data, missing == "Missing"), 
            aes(x = cN, y = cT), color = "black", fill = NA, size = 0.5, linetype = "dashed")  # Add diagonal cross for missing values

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_datadf <- as.data.frame(circ_data)

tally(~cM, data=circ_data, margins = TRUE)
circ_data$cM <- factor(circ_data$cM, levels = c("M0","M1"), labels = c("M0 (n=60)","M1 (n=2)"))
boxplot(ctDNA.Base.MTM~cM, data=circ_data, main="ctDNA pre-treatment MTM - cM", xlab="cM", ylab="MTM/mL", col="white",border="black", ylim = c(0, 500))
median_ctDNA.cM <- circ_data %>%
  group_by(cM) %>%
  summarise(median_ctDNA_Base_MTM = median(ctDNA.Base.MTM, na.rm = TRUE))
print(median_ctDNA.cM)
m4<-wilcox.test(ctDNA.Base.MTM ~ cM, data=circ_data, na.rm=TRUE, exact=FALSE, conf.int=TRUE)
print(m4)

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_datadf <- as.data.frame(circ_data)

tally(~p16.status, data=circ_data, margins = TRUE)
circ_data$p16.status <- factor(circ_data$p16.status, levels = c("Negative","Positive"), labels = c("p16 neg (n=23)","p16 pos (n=39)"))
boxplot(ctDNA.Base.MTM~p16.status, data=circ_data, main="ctDNA pre-treatment MTM - p16 status", xlab="p16 status", ylab="MTM/mL", col="white",border="black", ylim = c(0, 200))
median_ctDNA.p16 <- circ_data %>%
  group_by(p16.status) %>%
  summarise(median_ctDNA_Base_MTM = median(ctDNA.Base.MTM, na.rm = TRUE))
print(median_ctDNA.p16)
m5<-wilcox.test(ctDNA.Base.MTM ~ p16.status, data=circ_data, na.rm=TRUE, exact=FALSE, conf.int=TRUE)
print(m5)

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_datadf <- as.data.frame(circ_data)

tally(~Prim.Location, data=circ_data, margins = TRUE)
circ_data$Prim.Location <- factor(circ_data$Prim.Location, levels = c("Larynx/Hypopharynx","Oral cavity", "Oropharynx", "Other (paranasal sinus and nasopharyngeal)"))
boxplot(ctDNA.Base.MTM~Prim.Location, data=circ_data, main="ctDNA pre-treatment MTM - Tumor Location", xlab="Tumor Location", ylab="MTM/mL", col="white",border="black", ylim = c(0, 200))
median_ctDNA.loc <- circ_data %>%
  group_by(Prim.Location) %>%
  summarise(median_ctDNA_Base_MTM = median(ctDNA.Base.MTM, na.rm = TRUE))
print(median_ctDNA.loc)
pairwise_wilcox <- pairwise.wilcox.test(circ_data$ctDNA.Base.MTM, circ_data$Prim.Location, 
                                        p.adjust.method = "none", 
                                        exact = FALSE)

print(pairwise_wilcox)

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available == "TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Base != "",]
circ_data$Prim.Location <- factor(circ_data$Prim.Location, levels = c("Larynx/Hypopharynx","Oral cavity", "Oropharynx", "Other (paranasal sinus and nasopharyngeal)"), labels = c("LRX/HPRX","OC","PRX","Other"))
circ_data$ctDNA.Base.MTM <- as.numeric(circ_data$ctDNA.Base.MTM)
pl_levels <- levels(circ_data$Prim.Location)
p_value_matrix <- matrix(NA, nrow = length(pl_levels), ncol = length(pl_levels))
rownames(p_value_matrix) <- pl_levels
colnames(p_value_matrix) <- pl_levels

for (i in 1:length(pl_levels)) {
  for (j in i:length(pl_levels)) {
    if (i != j) {
      # Extract data for both groups
      data1 <- circ_data %>% filter(Prim.Location == pl_levels[i]) %>% pull(ctDNA.Base.MTM)
      data2 <- circ_data %>% filter(Prim.Location == pl_levels[j]) %>% pull(ctDNA.Base.MTM)
      
      # Perform Wilcoxon test and store p-value
      test_result <- wilcox.test(data1, data2, exact = FALSE)
      p_value_matrix[i, j] <- test_result$p.value
      p_value_matrix[j, i] <- test_result$p.value  # Make symmetric
    } else {
      p_value_matrix[i, j] <- 1  # Self-comparison = 1
    }
  }
}

p_value_matrix[is.na(p_value_matrix)] <- 1.00
p_value_data <- melt(p_value_matrix)
colnames(p_value_data) <- c("pl1", "pl2", "p_value")
p_value_data <- p_value_data %>%
  mutate(
    significance = case_when(
      p_value < 0.001 ~ "***",
      p_value < 0.01 ~ "**",
      p_value < 0.05 ~ "*",
      TRUE ~ ""
    )
  )

ggplot(p_value_data, aes(x = pl1, y = pl2, fill = p_value)) +
  geom_tile(color = "white", size = 0.8) +  # Thicker grid lines for separation
  geom_text(aes(label = significance), color = "black", size = 6, fontface = "bold") +  # Significance markers
  scale_fill_gradient2(low = "blue", mid = "white", high = "red", midpoint = 0.05) +  # Gradient colors
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 12, face = "bold"),
        axis.text.y = element_text(size = 12, face = "bold"),
        panel.grid = element_blank()) +
  labs(title = "Pairwise Wilcoxon-Test P-Values (ctDNA.Base.MTM by Tumor Location)",
       x = "Tumor Location", y = "Tumor Location", fill = "P-Value")
```

#PFS by ctDNA status at MRD
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)~ctDNA.MRD, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.MRD) %>%
  summarise(
    Total = n(),
    Events = sum(PFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="PFS - ctDNA at MRD", ylab= "Progression-Free Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 12, 24, 36))
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PFS.Event <- factor(circ_data$PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression"))
contingency_table <- table(circ_data$ctDNA.MRD, circ_data$PFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA status at MRD", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Progression",
       caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("No Progression" = "blue", "Progression" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Progression label size
```

#OS by ctDNA status at MRD
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$OS.months, event = circ_data$OS.Event)~ctDNA.MRD, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.MRD) %>%
  summarise(
    Total = n(),
    Events = sum(OS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$OS.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="OS - ctDNA at MRD", ylab= "Overall Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(12, 24, 36))
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$OS.Event <- factor(circ_data$OS.Event, levels = c("FALSE", "TRUE"), labels = c("Alive", "Deceased"))
contingency_table <- table(circ_data$ctDNA.MRD, circ_data$OS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA status at MRD", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Living Status",
       caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("Alive" = "blue", "Deceased" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Progression label size
```

#PFS by ctDNA status at MRD - exclude pts with no subsequent adj. treatment
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[!(circ_data$Surgery == TRUE & circ_data$Chemotherapy == FALSE), ]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)~ctDNA.MRD, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.MRD) %>%
  summarise(
    Total = n(),
    Events = sum(PFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="PFS - ctDNA at MRD", ylab= "Progression-Free Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 12, 24, 36))
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```

#PFS by ctDNA status at MRD Stage I/II
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$cStage=="I/II",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)~ctDNA.MRD, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.MRD) %>%
  summarise(
    Total = n(),
    Events = sum(PFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="PFS - ctDNA at MRD Stage I/II", ylab= "Progression-Free Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 12, 24, 36))
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PFS.Event <- factor(circ_data$PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression"))
contingency_table <- table(circ_data$ctDNA.MRD, circ_data$PFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA status at MRD Stage I/II", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Progression",
       caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("No Progression" = "blue", "Progression" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Progression label size
```

#PFS by ctDNA status at MRD Stage III/IV
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$cStage=="III/IV",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)~ctDNA.MRD, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.MRD) %>%
  summarise(
    Total = n(),
    Events = sum(PFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="PFS - ctDNA at MRD Stage III/IV", ylab= "Progression-Free Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 12, 24, 36))
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PFS.Event <- factor(circ_data$PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression"))
contingency_table <- table(circ_data$ctDNA.MRD, circ_data$PFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA status at MRD Stage I/II", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Progression",
       caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("No Progression" = "blue", "Progression" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Progression label size
```

#PFS by ctDNA at MRD p16(+)
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$p16.status=="Positive",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)~ctDNA.MRD, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.MRD) %>%
  summarise(
    Total = n(),
    Events = sum(PFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="PFS - ctDNA at MRD p16(+)", ylab= "Progression-Free Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 12, 24, 36))
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PFS.Event <- factor(circ_data$PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression"))
contingency_table <- table(circ_data$ctDNA.MRD, circ_data$PFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA status at MRD p16(+)", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Progression",
       caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("No Progression" = "blue", "Progression" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Progression label size
```

#PFS by ctDNA at MRD p16(-)
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$p16.status=="Negative",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)~ctDNA.MRD, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.MRD) %>%
  summarise(
    Total = n(),
    Events = sum(PFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="PFS - ctDNA at MRD p16(-)", ylab= "Progression-Free Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 12, 24, 36))
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PFS.Event <- factor(circ_data$PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression"))
contingency_table <- table(circ_data$ctDNA.MRD, circ_data$PFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA status at MRD p16(-)", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Progression",
       caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("No Progression" = "blue", "Progression" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Progression label size
```

#PFS by ctDNA status at surveillance
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)~ctDNA.Surveillance, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.Surveillance) %>%
  summarise(
    Total = n(),
    Events = sum(PFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Surveillance, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="PFS - ctDNA at Surveillance", ylab= "Progression-Free Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 12, 24, 36))
circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.Surveillance, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PFS.Event <- factor(circ_data$PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression"))
contingency_table <- table(circ_data$ctDNA.Surveillance, circ_data$PFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA status at Surveillance", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Progression",
       caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("No Progression" = "blue", "Progression" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Progression label size
```

#OS by ctDNA status at surveillance
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$OS.months, event = circ_data$OS.Event)~ctDNA.Surveillance, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.Surveillance) %>%
  summarise(
    Total = n(),
    Events = sum(OS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$OS.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Surveillance, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="OS - ctDNA at Surveillance", ylab= "Overall Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(12, 24, 36))
circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.Surveillance, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$OS.Event <- factor(circ_data$OS.Event, levels = c("FALSE", "TRUE"), labels = c("Alive", "Deceased"))
contingency_table <- table(circ_data$ctDNA.Surveillance, circ_data$OS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA status at Surveillance", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Living Status",
       caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("Alive" = "blue", "Deceased" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Progression label size
```

#Median numbers of time points and lead time in the longitudinal setting
```{r}
# Load the dataset
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_datadf <- as.data.frame(circ_data)

median_Nsurvtps <- median(circ_datadf$Nsurvtps, na.rm = TRUE)
min_Nsurvtps <- min(circ_datadf$Nsurvtps, na.rm = TRUE)
max_Nsurvtps <- max(circ_datadf$Nsurvtps, na.rm = TRUE)

cat(sprintf("Median # of surveillance time points: %d (%d-%d)\n", 
            median_Nsurvtps, min_Nsurvtps, max_Nsurvtps))

circ_datadf$LeadTime_Months <- circ_datadf$LeadTime / 30.437
median_LeadTime <- median(circ_datadf$LeadTime_Months, na.rm = TRUE)
min_LeadTime <- min(circ_datadf$LeadTime_Months, na.rm = TRUE)
max_LeadTime <- max(circ_datadf$LeadTime_Months, na.rm = TRUE)
cat(sprintf("Longitudinally, ctDNA positivity preceded progression by a median of %.2f mo (%.2f–%.2f)\n", 
            median_LeadTime, min_LeadTime, max_LeadTime))
```

#Time-dependent analysis for PFS in longitudinal time points
```{r}
rm(list=ls())
setwd("~/Downloads")
dt <- read_xlsx("CLIA HNSCC Peddada Clinical Data_Time dependent.xlsx") |>
  clean_names() |>
  mutate(across(.cols = c(window_start_date,dfs_date,
                          surveillance_1_date:surveillance_12_date), 
                .fns = ~ as_date(as.Date(.x, format = "%Y-%m-%d"))))

dt_biomarker <- dt |>
  select(pts_id, ct_dna_surveillance_available,
         window_start_date,
         surveillance_1_status:surveillance_12_date) |>
  filter(ct_dna_surveillance_available) |>
  pivot_longer(cols = surveillance_1_status:surveillance_12_date,
               names_to = c("visit_number", ".value"),
               names_pattern = "surveillance_(.)_(.*)") |>
  mutate(biomarker_time = day(days(date - window_start_date))) |>
  select(pts_id, biomarker_time, biomarker_status = status) |>
  filter(!is.na(biomarker_time))

glimpse(dt_biomarker)

dt_survival <- dt |>
  select(pts_id, ct_dna_surveillance_available,
         window_start_date:dfs_date, dfs_event) |>  # Added dfs_event here
  filter(ct_dna_surveillance_available) |>
  mutate(dfs_time = (dfs_date - window_start_date),
         dfs_time = day(days(dfs_time)),
         dfs_event = as.numeric(dfs_event)) |>
  select(pts_id, dfs_time, dfs_event)

glimpse(dt_survival)

aux <- dt_survival %>% 
  filter(dfs_time <= 0)

tab <- left_join(aux, dt) |>
  select(pts_id, window_start_date, dfs_time, dfs_date,
         surveillance_1_date:surveillance_12_date) |>
  mutate(across(.cols = dfs_date:surveillance_12_date, 
                .fns = ~ as_date(.x))) |>
  select(pts_id, window_start_date, dfs_date, dfs_time)

datatable(tab, filter = "top")

dt_survival <- dt_survival |>
  filter(dfs_time > 0)

aux <- dt |>
  select(pts_id, ct_dna_surveillance_available,
         window_start_date, dfs_date,
         surveillance_1_date:surveillance_12_date) |>
  mutate(across(.cols = surveillance_1_date:surveillance_12_date, 
                .fns = ~ .x - window_start_date)) |>
  mutate(across(.cols = surveillance_1_date:surveillance_12_date, 
                .fns = ~ .x < 0)) |>
  rowwise() |>
  mutate(sum_neg = 
           sum(c_across(surveillance_1_date:surveillance_12_date),
               na.rm = TRUE))  |>
  select(pts_id, sum_neg)

tab <- left_join(aux, dt) |>
  filter(sum_neg > 0) |>
  select(pts_id, sum_neg, window_start_date,
         surveillance_1_date:surveillance_12_date) |>
  mutate(across(.cols = window_start_date:surveillance_12_date, 
                .fns = ~ as_date(.x))) 

datatable(tab, filter = "top")

aux <- dt |>
  select(pts_id, ct_dna_surveillance_available,
         window_start_date, dfs_date,
         surveillance_1_date:surveillance_12_date) |>
  mutate(across(.cols = dfs_date:surveillance_12_date, 
                .fns = ~ .x - window_start_date)) |>
  mutate(across(.cols = surveillance_2_date:surveillance_12_date,
                .fns = ~ dfs_date < .x)) |>
  rowwise() |>
  mutate(n_biomarker_after_event = sum(c_across(surveillance_2_date:
                                                  surveillance_12_date), 
                                       na.rm = TRUE)) |>
  mutate(across(.cols = surveillance_1_date:surveillance_12_date,
                .fns = ~ !is.na(.x))) |>
  mutate(total_biomarker = sum(c_across(surveillance_2_date:
                                          surveillance_12_date), 
                               na.rm = TRUE)) |>
  select(pts_id, n_biomarker_after_event, total_biomarker)

temp <- aux |> 
  select(-pts_id) |> 
  group_by(n_biomarker_after_event, total_biomarker) |>  # Direct grouping
  summarise(freq = n(), .groups = "drop")  # Drop groups after summarization


tab <- left_join(aux, dt) |>
  select(pts_id, n_biomarker_after_event, total_biomarker, 
         dfs_date,
         surveillance_2_date:surveillance_12_date) |>
  mutate(across(.cols = dfs_date:surveillance_12_date, 
                .fns = ~ as_date(.x))) |>
  filter(n_biomarker_after_event > 0)
datatable(tab, filter = "top")

aux <- tmerge(data1 = dt_survival, 
              data2 = dt_survival,
              id = pts_id, 
              dfs_event = event(dfs_time, dfs_event))
dt_final <- tmerge(data1 = aux, 
                   data2 = dt_biomarker,
                   id = pts_id, 
                   biomarker_status = 
                     tdc(biomarker_time, biomarker_status))

datatable(dt_final, filter = "top")

# Syntax if there is not time-dependent covariate
# fit <- coxph(Surv(dfs_time, dfs_event) ~ biomarker_status,
#              data = dt_final)
# summary(fit)

fit <- coxph(Surv(tstart, tstop, dfs_event) ~ biomarker_status,
             data = dt_final)
summary(fit)
cox_fit_summary <- summary(fit)

#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```


#Time-dependent analysis for OS in longitudinal time points
```{r}
rm(list=ls())
setwd("~/Downloads")
dt <- read_xlsx("CLIA HNSCC Peddada Clinical Data_Time dependent.xlsx") |>
  clean_names() |>
  mutate(across(.cols = c(window_start_date,os_date,
                          surveillance_1_date:surveillance_12_date), 
                .fns = ~ as_date(as.Date(.x, format = "%Y-%m-%d"))))

dt_biomarker <- dt |>
  select(pts_id, ct_dna_surveillance_available,
         window_start_date,
         surveillance_1_status:surveillance_12_date) |>
  filter(ct_dna_surveillance_available) |>
  pivot_longer(cols = surveillance_1_status:surveillance_12_date,
               names_to = c("visit_number", ".value"),
               names_pattern = "surveillance_(.)_(.*)") |>
  mutate(biomarker_time = day(days(date - window_start_date))) |>
  select(pts_id, biomarker_time, biomarker_status = status) |>
  filter(!is.na(biomarker_time))

glimpse(dt_biomarker)

dt_survival <- dt |>
  select(pts_id, ct_dna_surveillance_available,
         window_start_date:os_date, os_event) |>  # Added os_event here
  filter(ct_dna_surveillance_available) |>
  mutate(os_time = (os_date - window_start_date),
         os_time = day(days(os_time)),
         os_event = as.numeric(os_event)) |>
  select(pts_id, os_time, os_event)

glimpse(dt_survival)

aux <- dt_survival %>% 
  filter(os_time <= 0)

tab <- left_join(aux, dt) |>
  select(pts_id, window_start_date, os_time, os_date,
         surveillance_1_date:surveillance_12_date) |>
  mutate(across(.cols = os_date:surveillance_12_date, 
                .fns = ~ as_date(.x))) |>
  select(pts_id, window_start_date, os_date, os_time)

datatable(tab, filter = "top")

dt_survival <- dt_survival |>
  filter(os_time > 0)

aux <- dt |>
  select(pts_id, ct_dna_surveillance_available,
         window_start_date, os_date,
         surveillance_1_date:surveillance_12_date) |>
  mutate(across(.cols = surveillance_1_date:surveillance_12_date, 
                .fns = ~ .x - window_start_date)) |>
  mutate(across(.cols = surveillance_1_date:surveillance_12_date, 
                .fns = ~ .x < 0)) |>
  rowwise() |>
  mutate(sum_neg = 
           sum(c_across(surveillance_1_date:surveillance_12_date),
               na.rm = TRUE))  |>
  select(pts_id, sum_neg)

tab <- left_join(aux, dt) |>
  filter(sum_neg > 0) |>
  select(pts_id, sum_neg, window_start_date,
         surveillance_1_date:surveillance_12_date) |>
  mutate(across(.cols = window_start_date:surveillance_12_date, 
                .fns = ~ as_date(.x))) 

datatable(tab, filter = "top")

aux <- dt |>
  select(pts_id, ct_dna_surveillance_available,
         window_start_date, os_date,
         surveillance_1_date:surveillance_12_date) |>
  mutate(across(.cols = os_date:surveillance_12_date, 
                .fns = ~ .x - window_start_date)) |>
  mutate(across(.cols = surveillance_2_date:surveillance_12_date,
                .fns = ~ os_date < .x)) |>
  rowwise() |>
  mutate(n_biomarker_after_event = sum(c_across(surveillance_2_date:
                                                  surveillance_12_date), 
                                       na.rm = TRUE)) |>
  mutate(across(.cols = surveillance_1_date:surveillance_12_date,
                .fns = ~ !is.na(.x))) |>
  mutate(total_biomarker = sum(c_across(surveillance_2_date:
                                          surveillance_12_date), 
                               na.rm = TRUE)) |>
  select(pts_id, n_biomarker_after_event, total_biomarker)

temp <- aux |> 
  select(-pts_id) |> 
  group_by(n_biomarker_after_event, total_biomarker) |>  # Direct grouping
  summarise(freq = n(), .groups = "drop")  # Drop groups after summarization


tab <- left_join(aux, dt) |>
  select(pts_id, n_biomarker_after_event, total_biomarker, 
         os_date,
         surveillance_2_date:surveillance_12_date) |>
  mutate(across(.cols = os_date:surveillance_12_date, 
                .fns = ~ as_date(.x))) |>
  filter(n_biomarker_after_event > 0)
datatable(tab, filter = "top")

aux <- tmerge(data1 = dt_survival, 
              data2 = dt_survival,
              id = pts_id, 
              os_event = event(os_time, os_event))
dt_final <- tmerge(data1 = aux, 
                   data2 = dt_biomarker,
                   id = pts_id, 
                   biomarker_status = 
                     tdc(biomarker_time, biomarker_status))

datatable(dt_final, filter = "top")

# Syntax if there is not time-dependent covariate
# fit <- coxph(Surv(os_time, os_event) ~ biomarker_status,
#              data = dt_final)
# summary(fit)

fit <- coxph(Surv(tstart, tstop, os_event) ~ biomarker_status,
             data = dt_final)
summary(fit)
cox_fit_summary <- summary(fit)

#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```


#PFS by ctDNA status at surveillance Stage I/II
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$cStage=="I/II",]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)~ctDNA.Surveillance, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.Surveillance) %>%
  summarise(
    Total = n(),
    Events = sum(PFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Surveillance, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = TRUE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="PFS - ctDNA at Surveillance Stage I/II", ylab= "Progression-Free Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 12, 24, 36))
circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxphf(surv_object ~ ctDNA.Surveillance, data=circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PFS.Event <- factor(circ_data$PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression"))
contingency_table <- table(circ_data$ctDNA.Surveillance, circ_data$PFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA status at Surveillance Stage I/II", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Progression",
       caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("No Progression" = "blue", "Progression" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Progression label size
```

#PFS by ctDNA status at surveillance Stage III/IV
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$cStage=="III/IV",]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)~ctDNA.Surveillance, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.Surveillance) %>%
  summarise(
    Total = n(),
    Events = sum(PFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Surveillance, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="PFS - ctDNA at Surveillance Stage III/IV", ylab= "Progression-Free Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 12, 24, 36))
circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.Surveillance, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PFS.Event <- factor(circ_data$PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression"))
contingency_table <- table(circ_data$ctDNA.Surveillance, circ_data$PFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA status at Surveillance Stage III/IV", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Progression",
       caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("No Progression" = "blue", "Progression" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Progression label size
```

#PFS by ctDNA status at surveillance p16(+)
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$p16.status=="Positive",]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)~ctDNA.Surveillance, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.Surveillance) %>%
  summarise(
    Total = n(),
    Events = sum(PFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Surveillance, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = TRUE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="PFS - ctDNA at Surveillance p16(+)", ylab= "Progression-Free Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 12, 24, 36))
circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxphf(surv_object ~ ctDNA.Surveillance, data=circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PFS.Event <- factor(circ_data$PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression"))
contingency_table <- table(circ_data$ctDNA.Surveillance, circ_data$PFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA status at Surveillance p16(+)", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Progression",
       caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("No Progression" = "blue", "Progression" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Progression label size
```

#PFS by ctDNA status at surveillance p16(-)
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$p16.status=="Negative",]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)~ctDNA.Surveillance, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.Surveillance) %>%
  summarise(
    Total = n(),
    Events = sum(PFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Surveillance, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="PFS - ctDNA at Surveillance p16(-)", ylab= "Progression-Free Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 12, 24, 36))
circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.Surveillance, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PFS.Event <- factor(circ_data$PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression"))
contingency_table <- table(circ_data$ctDNA.Surveillance, circ_data$PFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA status at Surveillance p16(-)", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Progression",
       caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("No Progression" = "blue", "Progression" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Progression label size
```

#Multivariate cox regression for PFS ctDNA status at surveillance
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]

circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"), labels = c("Negative", "Positive"))
circ_data$cStage <- factor(circ_data$cStage, levels = c("I/II", "III/IV"))
circ_data$p16.status <- factor(circ_data$p16.status, levels = c("Negative", "Positive"))
circ_data$Prim.Location <- factor(circ_data$Prim.Location, levels = c("Oropharynx", "Larynx/Hypopharynx", "Oral cavity", "Other (paranasal sinus and nasopharyngeal)"))
surv_object <- Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event) 
cox_fit <- coxph(surv_object ~ ctDNA.Surveillance + cStage + p16.status + Prim.Location, data=circ_data) 
ggforest(cox_fit, data = circ_data, main = "Multivariate Regression Model for PFS", refLabel = "Reference Group")
test.ph <- cox.zph(cox_fit)
```


#ctDNA and MTM/mL Dynamics for pts at surveillance window
```{r}
#Dynamics and MTM/mL plots for patients with ctDNA negative at surveillance
rm(list=ls())
setwd("~/Downloads")
df <- read.csv("CLIA HNSCC ctDNA MTM.csv", stringsAsFactors = FALSE)
df <- df[df$ctDNA.Surveillance=="NEGATIVE",]

df$PFS.Event <- ifelse(df$PFS.Event %in% c("No", "no", "FALSE", "False", "0"), FALSE,
                       ifelse(df$PFS.Event %in% c("Yes", "yes", "TRUE", "True", "1"), TRUE, NA))
df$PFS.Event <- factor(df$PFS.Event, levels = c(FALSE, TRUE))
df <- df %>%
  group_by(PatientName) %>%
  filter(n() >= 2) %>% #keep only pts with at least 2 post-surgery time points
  ungroup()

num_unique <- length(unique(df$PatientName))
cat("Number of unique patients:", num_unique, "\n")

df_patient_pfs <- df %>%
  group_by(PatientName) %>%
  dplyr::summarize(
    PFS_True = any(PFS.Event == TRUE, na.rm = TRUE),
    PFS_False = all(PFS.Event == FALSE, na.rm = TRUE)
  )

num_true <- sum(df_patient_pfs$PFS_True)
num_false <- sum(df_patient_pfs$PFS_False)

cat("Number of unique patients with Event:", num_true, "\n")
cat("Number of unique patients with No Event:", num_false, "\n")

p <- ggplot(df, aes(x = date.diff.months, 
                    y = MTM.mL, 
                    group = PatientName, 
                    color = PFS.Event)) +
  geom_line() +      # Connect timepoints for each patient
  geom_point() +     # Add points for each timepoint
  # Use a log10 scale for the y-axis with specified breaks
  scale_y_log10(breaks = c(0.01, 0.1, 1, 10, 100),
                labels = c("0.01","0.1", "1", "10", "100")) +
  scale_x_continuous(breaks = seq(0, max(df$date.diff.months, na.rm = TRUE), by = 6)) +
  scale_color_manual(values = c("FALSE" = "blue", "TRUE" = "red")) +
  labs(
    x = "Time Since Surgery or start of definitive treatment (months)",
    y = "Mean Tumor Molecules per mL (MTM/mL)",
    color = "PFS Event"
  ) +
  theme_minimal()
print(p)

#Dynamics and MTM/mL plots for patients with ctDNA positive at surveillance
rm(list=ls())
setwd("~/Downloads")
df <- read.csv("CLIA HNSCC ctDNA MTM.csv", stringsAsFactors = FALSE)
df <- df[df$ctDNA.Surveillance=="POSITIVE",]

df$PFS.Event <- ifelse(df$PFS.Event %in% c("No", "no", "FALSE", "False", "0"), FALSE,
                       ifelse(df$PFS.Event %in% c("Yes", "yes", "TRUE", "True", "1"), TRUE, NA))
df$PFS.Event <- factor(df$PFS.Event, levels = c(FALSE, TRUE))
df <- df %>%
  group_by(PatientName) %>%
  filter(n() >= 2) %>% #keep only pts with at least 2 post-surgery time points
  ungroup()

num_unique <- length(unique(df$PatientName))
cat("Number of unique patients:", num_unique, "\n")

df_patient_pfs <- df %>%
  group_by(PatientName) %>%
  dplyr::summarize(
    PFS_True = any(PFS.Event == TRUE, na.rm = TRUE),
    PFS_False = all(PFS.Event == FALSE, na.rm = TRUE)
  )

num_true <- sum(df_patient_pfs$PFS_True)
num_false <- sum(df_patient_pfs$PFS_False)

cat("Number of unique patients with Event:", num_true, "\n")
cat("Number of unique patients with No Event:", num_false, "\n")

p <- ggplot(df, aes(x = date.diff.months, 
                    y = MTM.mL, 
                    group = PatientName, 
                    color = PFS.Event)) +
  geom_line() +      # Connect timepoints for each patient
  geom_point() +     # Add points for each timepoint
  # Use a log10 scale for the y-axis with specified breaks
  scale_y_log10(breaks = c(0.01, 0.1, 1, 10, 100),
                labels = c("0.01","0.1", "1", "10", "100")) +
  scale_x_continuous(breaks = seq(0, max(df$date.diff.months, na.rm = TRUE), by = 6)) +
  scale_color_manual(values = c("FALSE" = "blue", "TRUE" = "red")) +
  labs(
    x = "Time Since Surgery or start of definitive treatment (months)",
    y = "Mean Tumor Molecules per mL (MTM/mL)",
    color = "PFS Event"
  ) +
  theme_minimal()
print(p)
```

#ctDNA and MTM/mL Dynamics for pts at surveillance window (excluding baseline & post-progression samples)
```{r}
#Dynamics and MTM/mL plots for patients with ctDNA negative at surveillance
rm(list=ls())
setwd("~/Downloads")
df <- read.csv("CLIA HNSCC ctDNA MTM.csv", stringsAsFactors = FALSE)
df <- df[!(df$ctDNA.Window %in% c("Baseline", "Post-PD")), ]
df <- df[df$ctDNA.Surveillance=="NEGATIVE",]

df$PFS.Event <- ifelse(df$PFS.Event %in% c("No", "no", "FALSE", "False", "0"), FALSE,
                       ifelse(df$PFS.Event %in% c("Yes", "yes", "TRUE", "True", "1"), TRUE, NA))
df$PFS.Event <- factor(df$PFS.Event, levels = c(FALSE, TRUE))
df <- df %>%
  group_by(PatientName) %>%
  filter(n() >= 2) %>% #keep only pts with at least 2 post-surgery time points
  ungroup()

num_unique <- length(unique(df$PatientName))
cat("Number of unique patients:", num_unique, "\n")

p <- ggplot(df, aes(x = date.diff.months, 
                    y = MTM.mL, 
                    group = PatientName, 
                    color = PFS.Event)) +
  geom_line() +      # Connect timepoints for each patient
  geom_point() +     # Add points for each timepoint
  # Use a log10 scale for the y-axis with specified breaks
  scale_y_log10(breaks = c(0.01, 0.1, 1, 10, 100),
                labels = c("0.01","0.1", "1", "10", "100")) +
  scale_x_continuous(breaks = seq(0, max(df$date.diff.months, na.rm = TRUE), by = 6)) +
  scale_color_manual(values = c("FALSE" = "blue", "TRUE" = "red")) +
  labs(
    x = "Time Since Surgery or start of definitive treatment (months)",
    y = "Mean Tumor Molecules per mL (MTM/mL)",
    color = "PFS Event"
  ) +
  theme_minimal()
print(p)

#Dynamics and MTM/mL plots for patients with ctDNA positive at surveillance
rm(list=ls())
setwd("~/Downloads")
df <- read.csv("CLIA HNSCC ctDNA MTM.csv", stringsAsFactors = FALSE)
df <- df[!(df$ctDNA.Window %in% c("Baseline", "Post-PD")), ]
df <- df[df$ctDNA.Surveillance=="POSITIVE",]

df$PFS.Event <- ifelse(df$PFS.Event %in% c("No", "no", "FALSE", "False", "0"), FALSE,
                       ifelse(df$PFS.Event %in% c("Yes", "yes", "TRUE", "True", "1"), TRUE, NA))
df$PFS.Event <- factor(df$PFS.Event, levels = c(FALSE, TRUE))
df <- df %>%
  group_by(PatientName) %>%
  filter(n() >= 2) %>% #keep only pts with at least 2 post-surgery time points
  ungroup()

num_unique <- length(unique(df$PatientName))
cat("Number of unique patients:", num_unique, "\n")

p <- ggplot(df, aes(x = date.diff.months, 
                    y = MTM.mL, 
                    group = PatientName, 
                    color = PFS.Event)) +
  geom_line() +      # Connect timepoints for each patient
  geom_point() +     # Add points for each timepoint
  # Use a log10 scale for the y-axis with specified breaks
  scale_y_log10(breaks = c(0.01, 0.1, 1, 10, 100),
                labels = c("0.01","0.1", "1", "10", "100")) +
  scale_x_continuous(breaks = seq(0, max(df$date.diff.months, na.rm = TRUE), by = 6)) +
  scale_color_manual(values = c("FALSE" = "blue", "TRUE" = "red")) +
  labs(
    x = "Time Since Surgery or start of definitive treatment (months)",
    y = "Mean Tumor Molecules per mL (MTM/mL)",
    color = "PFS Event"
  ) +
  theme_minimal()
print(p)
```

#PFS by ctDNA status at surveillance for pts with MRD & Surveillance time points available
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.complete=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)~ctDNA.Surveillance, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.Surveillance) %>%
  summarise(
    Total = n(),
    Events = sum(PFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Surveillance, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="PFS - ctDNA at Surveillance", ylab= "Progression-Free Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 12, 24, 36))
circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.Surveillance, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PFS.Event <- factor(circ_data$PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression"))
contingency_table <- table(circ_data$ctDNA.Surveillance, circ_data$PFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA status at Surveillance", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Progression",
       caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("No Progression" = "blue", "Progression" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Progression label size
```

#Time-dependent analysis for PFS in longitudinal time points for pts with MRD & Surveillance time points available
```{r}
rm(list=ls())
setwd("~/Downloads")
dt <- read_xlsx("CLIA HNSCC Peddada Clinical Data_Time dependent.xlsx") |>
  clean_names() |>
  mutate(across(.cols = c(window_start_date,dfs_date,
                          surveillance_1_date:surveillance_12_date), 
                .fns = ~ as_date(as.Date(.x, format = "%Y-%m-%d"))))

dt_biomarker <- dt |>
  select(pts_id, ct_dna_complete,
         window_start_date,
         surveillance_1_status:surveillance_12_date) |>
  filter(ct_dna_complete) |>
  pivot_longer(cols = surveillance_1_status:surveillance_12_date,
               names_to = c("visit_number", ".value"),
               names_pattern = "surveillance_(.)_(.*)") |>
  mutate(biomarker_time = day(days(date - window_start_date))) |>
  select(pts_id, biomarker_time, biomarker_status = status) |>
  filter(!is.na(biomarker_time))

glimpse(dt_biomarker)

dt_survival <- dt |>
  select(pts_id, ct_dna_complete,
         window_start_date:dfs_date, dfs_event) |>  # Added dfs_event here
  filter(ct_dna_complete) |>
  mutate(dfs_time = (dfs_date - window_start_date),
         dfs_time = day(days(dfs_time)),
         dfs_event = as.numeric(dfs_event)) |>
  select(pts_id, dfs_time, dfs_event)

glimpse(dt_survival)

aux <- dt_survival %>% 
  filter(dfs_time <= 0)

tab <- left_join(aux, dt) |>
  select(pts_id, window_start_date, dfs_time, dfs_date,
         surveillance_1_date:surveillance_12_date) |>
  mutate(across(.cols = dfs_date:surveillance_12_date, 
                .fns = ~ as_date(.x))) |>
  select(pts_id, window_start_date, dfs_date, dfs_time)

datatable(tab, filter = "top")

dt_survival <- dt_survival |>
  filter(dfs_time > 0)

aux <- dt |>
  select(pts_id, ct_dna_complete,
         window_start_date, dfs_date,
         surveillance_1_date:surveillance_12_date) |>
  mutate(across(.cols = surveillance_1_date:surveillance_12_date, 
                .fns = ~ .x - window_start_date)) |>
  mutate(across(.cols = surveillance_1_date:surveillance_12_date, 
                .fns = ~ .x < 0)) |>
  rowwise() |>
  mutate(sum_neg = 
           sum(c_across(surveillance_1_date:surveillance_12_date),
               na.rm = TRUE))  |>
  select(pts_id, sum_neg)

tab <- left_join(aux, dt) |>
  filter(sum_neg > 0) |>
  select(pts_id, sum_neg, window_start_date,
         surveillance_1_date:surveillance_12_date) |>
  mutate(across(.cols = window_start_date:surveillance_12_date, 
                .fns = ~ as_date(.x))) 

datatable(tab, filter = "top")

aux <- dt |>
  select(pts_id, ct_dna_complete,
         window_start_date, dfs_date,
         surveillance_1_date:surveillance_12_date) |>
  mutate(across(.cols = dfs_date:surveillance_12_date, 
                .fns = ~ .x - window_start_date)) |>
  mutate(across(.cols = surveillance_2_date:surveillance_12_date,
                .fns = ~ dfs_date < .x)) |>
  rowwise() |>
  mutate(n_biomarker_after_event = sum(c_across(surveillance_2_date:
                                                  surveillance_12_date), 
                                       na.rm = TRUE)) |>
  mutate(across(.cols = surveillance_1_date:surveillance_12_date,
                .fns = ~ !is.na(.x))) |>
  mutate(total_biomarker = sum(c_across(surveillance_2_date:
                                          surveillance_12_date), 
                               na.rm = TRUE)) |>
  select(pts_id, n_biomarker_after_event, total_biomarker)

temp <- aux |> 
  select(-pts_id) |> 
  group_by(n_biomarker_after_event, total_biomarker) |>  # Direct grouping
  summarise(freq = n(), .groups = "drop")  # Drop groups after summarization


tab <- left_join(aux, dt) |>
  select(pts_id, n_biomarker_after_event, total_biomarker, 
         dfs_date,
         surveillance_2_date:surveillance_12_date) |>
  mutate(across(.cols = dfs_date:surveillance_12_date, 
                .fns = ~ as_date(.x))) |>
  filter(n_biomarker_after_event > 0)
datatable(tab, filter = "top")

aux <- tmerge(data1 = dt_survival, 
              data2 = dt_survival,
              id = pts_id, 
              dfs_event = event(dfs_time, dfs_event))
dt_final <- tmerge(data1 = aux, 
                   data2 = dt_biomarker,
                   id = pts_id, 
                   biomarker_status = 
                     tdc(biomarker_time, biomarker_status))

datatable(dt_final, filter = "top")

# Syntax if there is not time-dependent covariate
# fit <- coxph(Surv(dfs_time, dfs_event) ~ biomarker_status,
#              data = dt_final)
# summary(fit)

fit <- coxph(Surv(tstart, tstop, dfs_event) ~ biomarker_status,
             data = dt_final)
summary(fit)
cox_fit_summary <- summary(fit)

#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```


#Median numbers of time points and lead time in the longitudinal setting for pts with MRD & Surveillance time points available
```{r}
# Load the dataset
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.complete=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_datadf <- as.data.frame(circ_data)

median_Nsurvtps <- median(circ_datadf$Nsurvtps, na.rm = TRUE)
min_Nsurvtps <- min(circ_datadf$Nsurvtps, na.rm = TRUE)
max_Nsurvtps <- max(circ_datadf$Nsurvtps, na.rm = TRUE)

cat(sprintf("Median # of surveillance time points: %d (%d-%d)\n", 
            median_Nsurvtps, min_Nsurvtps, max_Nsurvtps))

circ_datadf$LeadTime_Months <- circ_datadf$LeadTime / 30.437
median_LeadTime <- median(circ_datadf$LeadTime_Months, na.rm = TRUE)
min_LeadTime <- min(circ_datadf$LeadTime_Months, na.rm = TRUE)
max_LeadTime <- max(circ_datadf$LeadTime_Months, na.rm = TRUE)
cat(sprintf("Longitudinally, ctDNA positivity preceded progression by a median of %.2f mo (%.2f–%.2f)\n", 
            median_LeadTime, min_LeadTime, max_LeadTime))
```

#PFS by ctDNA status anytime
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.anytime!="",]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)~ctDNA.anytime, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.anytime) %>%
  summarise(
    Total = n(),
    Events = sum(PFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.anytime, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="PFS - ctDNA anytime", ylab= "Progression-Free Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 12, 24, 36))
circ_data$ctDNA.anytime <- factor(circ_data$ctDNA.anytime, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.anytime, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

circ_data$ctDNA.anytime <- factor(circ_data$ctDNA.anytime, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PFS.Event <- factor(circ_data$PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression"))
contingency_table <- table(circ_data$ctDNA.anytime, circ_data$PFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA status anytime", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Progression",
       caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("No Progression" = "blue", "Progression" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Progression label size
```

#OS by ctDNA status anytime
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.anytime!="",]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$OS.months, event = circ_data$OS.Event)~ctDNA.anytime, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.anytime) %>%
  summarise(
    Total = n(),
    Events = sum(OS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$OS.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.anytime, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="OS - ctDNA anytime", ylab= "Overall Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(12, 24, 36))
circ_data$ctDNA.anytime <- factor(circ_data$ctDNA.anytime, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.anytime, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

circ_data$ctDNA.anytime <- factor(circ_data$ctDNA.anytime, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$OS.Event <- factor(circ_data$OS.Event, levels = c("FALSE", "TRUE"), labels = c("Alive", "Deceased"))
contingency_table <- table(circ_data$ctDNA.anytime, circ_data$OS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA status anytime", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Living Status",
       caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("Alive" = "blue", "Deceased" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Progression label size
```

#Median numbers of time points and lead time anytime post-surery or definitive treatment
```{r}
# Load the dataset
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.anytime!="",]
circ_datadf <- as.data.frame(circ_data)

median_Nsurvtps <- median(circ_datadf$Ntotaltps, na.rm = TRUE)
min_Nsurvtps <- min(circ_datadf$Ntotaltps, na.rm = TRUE)
max_Nsurvtps <- max(circ_datadf$Ntotaltps, na.rm = TRUE)

cat(sprintf("Median # of time points anytimes: %d (%d-%d)\n", 
            median_Nsurvtps, min_Nsurvtps, max_Nsurvtps))

circ_datadf$LeadTime_Months <- circ_datadf$Anytime.LeadTime / 30.437
median_LeadTime <- median(circ_datadf$LeadTime_Months, na.rm = TRUE)
min_LeadTime <- min(circ_datadf$LeadTime_Months, na.rm = TRUE)
max_LeadTime <- max(circ_datadf$LeadTime_Months, na.rm = TRUE)
cat(sprintf("Anytime post-surgery or start of definitive treatment, ctDNA positivity preceded progression by a median of %.2f mo (%.2f–%.2f)\n", 
            median_LeadTime, min_LeadTime, max_LeadTime))
```
